tesseract | Tesseract Open Source OCR Engine | Computer Vision library

 by   tesseract-ocr C++ Version: 5.3.0 License: Apache-2.0

kandi X-RAY | tesseract Summary

tesseract is a C++ library typically used in Artificial Intelligence, Computer Vision applications. tesseract has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.
This package contains an OCR engine - libtesseract and a command line program - tesseract. Tesseract 4 adds a new neural net (LSTM) based OCR engine which is focused on line recognition, but also still supports the legacy Tesseract OCR engine of Tesseract 3 which works by recognizing character patterns. Compatibility with Tesseract 3 is enabled by using the Legacy OCR Engine mode (--oem 0). It also needs [traineddata] files which support the legacy engine, for example those from the tessdata repository. The lead developer is Ray Smith. The maintainer is Zdenko Podobny. For a list of contributors see [AUTHORS] and GitHub’s log of [contributors] Tesseract has unicode (UTF-8) support, and can recognize more than 100 languages "out of the box". Tesseract supports various output formats: plain text, hOCR (HTML), PDF, invisible-text-only PDF, TSV and ALTO (the last one - since version 4.1.0). You should note that in many cases, in order to get better OCR results, you’ll need to [improve the quality] of the image you are giving Tesseract. This project does not include a GUI application. If you need one, please see the [3rdParty] documentation. Tesseract can be trained to recognize other languages. See [Tesseract Training] for more information.

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                        tesseract has a medium active ecosystem.
                        It has 49754 star(s) with 8428 fork(s). There are 1668 watchers for this library.
                        There were 2 major release(s) in the last 6 months.
                        There are 379 open issues and 2101 have been closed. On average issues are closed in 105 days. There are 15 open pull requests and 0 closed requests.
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                        The latest version of tesseract is 5.3.0
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                                                tesseract code analysis shows 0 unresolved vulnerabilities.
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                                                            tesseract is licensed under the Apache-2.0 License. This license is Permissive.
                                                            Permissive licenses have the least restrictions, and you can use them in most projects.
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                                                                        Installation instructions are not available. Examples and code snippets are available.
                                                                        It has 1158 lines of code, 88 functions and 14 files.
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                                                                                  tesseract Key Features

                                                                                  For the latest online version of the README.md see:.

                                                                                  tesseract Examples and Code Snippets

                                                                                  No Code Snippets are available at this moment for tesseract.
                                                                                  Community Discussions

                                                                                  Trending Discussions on tesseract

                                                                                  Tesseract very low detection quality
                                                                                  chevron right
                                                                                  Pytesseract not working for low resolution images
                                                                                  chevron right
                                                                                  Error Could not load file or assembly Tesseract on Ubuntu using .Net Core 3.1
                                                                                  chevron right
                                                                                  ocrmypdf - could not find source-pdf?
                                                                                  chevron right
                                                                                  How can I maximise the reliability of tesseract ocr for text recognition as much as possible?
                                                                                  chevron right
                                                                                  Tesseract OCR gives really bad output even with typed text
                                                                                  chevron right
                                                                                  Stop TensorFlow from printing warning message
                                                                                  chevron right
                                                                                  Pytesseract image to string error messages in Colab
                                                                                  chevron right
                                                                                  tesseract detects only 4 words from image
                                                                                  chevron right
                                                                                  How to install Tesseract OCR on Databricks
                                                                                  chevron right


                                                                                  Tesseract very low detection quality
                                                                                  Asked 2022-Apr-17 at 21:56

                                                                                  Trying to read some data with tesseract but it's already strugling with date and time, so I created a minimal test case.


                                                                                  using namespace std;
                                                                                  using namespace cv;
                                                                                  int main(int argc, const char * argv[]) {
                                                                                      string outText, imPath = argv[1];
                                                                                      cv::Mat image_final = cv::imread(imPath, CV_8UC1);
                                                                                      tesseract::TessBaseAPI *api = new tesseract::TessBaseAPI();
                                                                                      api->Init(NULL, "eng", tesseract::OEM_LSTM_ONLY);
                                                                                      cv::adaptiveThreshold(image_final,image_final,255,ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY,11,2);
                                                                                      api->SetImage(image_final.data, image_final.cols, image_final.rows, 3, image_final.step);
                                                                                      api->SetVariable("tessedit_char_whitelist", "0123456789- :");
                                                                                      outText = string(api->GetUTF8Text());
                                                                                      std::istringstream iss(outText);
                                                                                      for (std::string line; std::getline(iss, line); ) {
                                                                                          if (!line.empty()) cout << line << endl;
                                                                                      cv::imwrite("out.png", image_final);
                                                                                      return 0;


                                                                                  1122-03-08 18:10
                                                                                  2122-030 18:10

                                                                                  I even tried to whitelist these characters (which will not be the case in the final version) but still getting very bad results.


                                                                                  Answered 2022-Apr-17 at 21:56

                                                                                  It looks like the main issue is setting bytes_per_pixel to 3 instead of 1 in api->SetImage.

                                                                                  The image after cv::adaptiveThreshold is 1 color channel (1 byte per pixel) and not 3.

                                                                                  Replace api->SetImage(image_final.data, image_final.cols, image_final.rows, 3, image_final.step); with:

                                                                                  api->SetImage(image_final.data, image_final.cols, image_final.rows, 1, image_final.step);

                                                                                  Replace cv::imread(imPath, CV_8UC1) with cv::imread(imPath, cv::IMREAD_GRAYSCALE)

                                                                                  You may also try replacing tesseract::PSM_AUTO_ONLY with tesseract::PSM_AUTO or tesseract::PSM_SINGLE_BLOCK.

                                                                                  According to the comment in the header file:

                                                                                  PSM_AUTO_ONLY = 2, ///< Automatic page segmentation, but no OSD, or OCR.

                                                                                  (Unless this is in purpose - I never used the C++ interface).

                                                                                  I have tried to reproduce the problem using pytesseract and Python, but I am getting an error when setting PSM to 2.
                                                                                  I am probably also using different version of Tesseract.

                                                                                  The result is perfect, and it supposed to be perfect with the image from your post.

                                                                                  Python code:

                                                                                  import cv2
                                                                                  from pytesseract import pytesseract
                                                                                  # Tesseract path
                                                                                  pytesseract.tesseract_cmd = "C:\\Program Files\\Tesseract-OCR\\tesseract.exe"
                                                                                  img = cv2.imread("out.png", cv2.IMREAD_GRAYSCALE)  # Read input image as Grayscale
                                                                                  text = pytesseract.image_to_string(img, config="-c tessedit"
                                                                                                                                 "_char_whitelist=' '0123456789-:"
                                                                                                                                 " --psm 3 "

                                                                                  2022-03-08 18:19:15

                                                                                  Source https://stackoverflow.com/questions/71905201


                                                                                  Pytesseract not working for low resolution images
                                                                                  Asked 2022-Feb-18 at 11:41

                                                                                  I am trying to read numbers from an image with 20x10 resolution. I know this question might be a duplicate. I've gone through most of the questions here on stack overflow but none of the answers seems to work for me. Here is the image I am trying to read text from:

                                                                                  Here is the my current code:

                                                                                  import pytesseract as pt
                                                                                  from PIL import Image
                                                                                  pt.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
                                                                                  img = Image.open('foo.PNG')
                                                                                  text = pt.image_to_string(img)

                                                                                  I am new to pytesseract and image processing. Any suggestion or help will be greatly appreciated.


                                                                                  Answered 2022-Feb-18 at 11:41

                                                                                  Actually, I have to say that tesseract is very touchy to play with. According to my experiences, I can easily say that if you -as a human- are not able to read a text clearly, you shouldn't expect tesseract to read it either.

                                                                                  First of all; to get better results, it is a must to make a good preprocessing. I strongly recommend anyone dealing with tesseract to check their documentation about Improving the quality.

                                                                                  In your case, problem is about the resolution. Is low resolution a reason for tesseract not to read a text ? Answer is absolutely yes. Documentation says:

                                                                                  Tesseract works best on images which have a DPI of at least 300 dpi, so it may be beneficial to resize images.

                                                                                  In here DPI means dots per inch and its suggested lower limit is 300 DPI which is higher than your image. When you resize the image to a higher resolution, for example 10 times bigger:

                                                                                  Now even if DPI satisfies, now you are losing the accuracy and getting noises.

                                                                                  Note: It also doesn't mean that higher resolution means better results. Please check here.

                                                                                  Note: If you really need to continue on these types of images, you may need to have a look at here. First you get higher resolution and then deblurring operation, this may help to figure it out.

                                                                                  Source https://stackoverflow.com/questions/69716295


                                                                                  Error Could not load file or assembly Tesseract on Ubuntu using .Net Core 3.1
                                                                                  Asked 2022-Feb-12 at 00:01

                                                                                  I am using Tesseract version 4.1.1 on dotnetcore 3.1 project which works perfectly on windows but when I publish it on ubuntu it throws the following error on this line

                                                                                  new TesseractEngine(Tessdatapath, LanguageCode, EngineMode.TesseractAndLstm);

                                                                                  Could not load file or assembly 'Tesseract, Version=, Culture=neutral, PublicKeyToken=null'. The system cannot find the file specified.

                                                                                  I copied the x64 & x86 dlls with the publish files and made sure they are on the same level with tessdata

                                                                                  I tried to install tesseract on ubuntu and copied the .so files inside the x64 & x86 folders but still no luck


                                                                                  Answered 2021-Nov-03 at 17:04

                                                                                  So here is how I fixed it

                                                                                  It turned out that system didnt display the correct error message because it couldnt use the library System.Drawing.Common which is not supported by Linux.

                                                                                  Fixed that by using libgdiplux the Linux implementation of System.Drawing.Common

                                                                                  sudo apt-get -f install libgdiplus

                                                                                  Then it displayed the correct message which is

                                                                                  Failed to find library "libleptonica-1.80.0.so" for platform x64.

                                                                                  To fix that I had to compile this leptonica version from here http://www.leptonica.org/download.html

                                                                                  this helped me to compile it http://www.leptonica.org/source/README.html

                                                                                  So now that I have "libleptonica-1.80.0.so" installed I created link inside my x64 folder to leptonica files following this comment Tesseract Issue #503

                                                                                  Source https://stackoverflow.com/questions/69813484


                                                                                  ocrmypdf - could not find source-pdf?
                                                                                  Asked 2022-Jan-15 at 19:26

                                                                                  i would like to use ocrmypdf to convert some pdf-file from a picture to a readable pdf -

                                                                                  Tried it with the following simple code: (the invoice.pdf is of course available in the same path as the python-script and the output.pdf should be generated)

                                                                                  import ocrmypdf
                                                                                  if __name__ == '__main__':
                                                                                    fn = r"C:\Users\Polzi\Documents\DEV\Python-Diverses\PDFOCR\invoice.pdf"
                                                                                    ocrmypdf.ocr(fn, 'output.pdf', deskew=True)

                                                                                  But unfortunately i get this error message:

                                                                                  $ python exPDFOCR.py
                                                                                  [WinError 2] Das System kann die angegebene Datei nicht finden
                                                                                  Traceback (most recent call last):
                                                                                    File "C:\Users\Polzi\Documents\DEV\Python-Diverses\PDFOCR\exPDFOCR.py", line 25, in 
                                                                                      ocrmypdf.ocr('invoice.pdf', 'output.pdf', deskew=True)
                                                                                    File "C:\Users\Polzi\Documents\DEV\.venv\testing\lib\site-packages\ocrmypdf\api.py", line 336, in ocr
                                                                                      check_options(options, plugin_manager)
                                                                                    File "C:\Users\Polzi\Documents\DEV\.venv\testing\lib\site-packages\ocrmypdf\_validation.py", line 271, in check_options
                                                                                      ocr_engine_languages = plugin_manager.hook.get_ocr_engine().languages(options)
                                                                                    File "C:\Users\Polzi\Documents\DEV\.venv\testing\lib\site-packages\ocrmypdf\builtin_plugins\tesseract_ocr.py", line 155, in languages
                                                                                      return tesseract.get_languages()
                                                                                    File "C:\Users\Polzi\Documents\DEV\.venv\testing\lib\site-packages\ocrmypdf\_exec\tesseract.py", line 143, in get_languages
                                                                                      proc = run(
                                                                                    File "C:\Users\Polzi\Documents\DEV\.venv\testing\lib\site-packages\ocrmypdf\subprocess\__init__.py", line 53, in run
                                                                                      proc = subprocess_run(args, env=env, **kwargs)
                                                                                    File "c:\users\polzi\appdata\local\programs\python\python39\lib\subprocess.py", line 505, in run
                                                                                      with Popen(*popenargs, **kwargs) as process:
                                                                                    File "c:\users\polzi\appdata\local\programs\python\python39\lib\subprocess.py", line 951, in __init__
                                                                                      self._execute_child(args, executable, preexec_fn, close_fds,
                                                                                    File "c:\users\polzi\appdata\local\programs\python\python39\lib\subprocess.py", line 1420, in _execute_child
                                                                                      hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
                                                                                  FileNotFoundError: [WinError 2] Das System kann die angegebene Datei nicht finden

                                                                                  Why can´t he find the file in the same folder as the py-file is executed?


                                                                                  Answered 2022-Jan-15 at 19:26

                                                                                  Sometimes the first error message may be misleading without a clear cause

                                                                                  In this case the primary message "The system cannot find the specified file"

                                                                                  Will lead a user to concentrate on why a filename is not correct, as in this case.

                                                                                  What the error should report is that a required file in the dependencies was not found. which can be caused by one or more Tesseract or related Leptonica / Language data files not in the correct location either due to no install or poor install.

                                                                                  It transpired that installing tesseract on windows from https://github.com/UB-Mannheim/tesseract/wiki "the script now works fine"

                                                                                  Note a missing dependency was the cause of a similar message here Import ocrmypdf in Visual Stdio Code in Python

                                                                                  Source https://stackoverflow.com/questions/70717279


                                                                                  How can I maximise the reliability of tesseract ocr for text recognition as much as possible?
                                                                                  Asked 2022-Jan-03 at 23:33

                                                                                  I am attempting to collect data from a shop in a game ( starbase ) in order to feed the data to a website in order to be able to display them as a candle stick chart

                                                                                  So far I have started using Tesseract OCR 5.0.0 and I have been running into issues as I cannot get the values reliably

                                                                                  I have seen that the images can be pre-processed in order to increase the reliability but I have run into a bottleneck as I am not familiar enough with Tesseract and OpenCV in order to know what to do more

                                                                                  Please note that since this is an in-game UI the images are going to be very constant as there is no colour variations / light changes / font size changes / ... I technically only need to get it to work once and that's it

                                                                                  Here are the steps I have taken so far and the results :

                                                                                  I have started by getting a screen of only the part of the UI I am interested in in order to remove as much clutter as possible

                                                                                  I have then set a threshold as shown here ( I will also be using the cropping part when doing the automation but I am not there yet ), set the language to English and the psm argument to 6 witch gives me the following code :

                                                                                  import cv2
                                                                                  import pytesseract
                                                                                  def clean_text(text):
                                                                                      ret = text.replace("\n\n", "\n")  # remove the blank lines
                                                                                      return ret
                                                                                  pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract'
                                                                                  img = cv2.imread('screens/ressources_list_array_1.png', 0)
                                                                                  thresh = 255 - cv2.threshold(img, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
                                                                                  print("======= Output")
                                                                                  print(clean_text(pytesseract.image_to_string(thresh, lang='eng', config='--psm 6')))
                                                                                  cv2.imshow('thresh', thresh)

                                                                                  Here is an example of the output I get :

                                                                                  ======= Output
                                                                                  Aegisium Ore 4490 456
                                                                                  Ajatite Ore 600 332
                                                                                  Arkanium Ore 84999 53
                                                                                  Bastium Ore 2350 421
                                                                                  Charodium Ore 5 280 366
                                                                                  Corazium Ore 39 896 212
                                                                                  Exorium Ore 5 380 112
                                                                                  Ice 980 141
                                                                                  Karnite Crystal ele) 111
                                                                                  Kutonium Ore 14 000 215
                                                                                  Lukium Ore 31 000 158
                                                                                  Nhurgite Crystal 3144 64
                                                                                  Surtrite Crystal 4198 70
                                                                                  Valkite Ore 545 150
                                                                                  Vokarium Ore 1850 415
                                                                                  Ymrium Ore 69 899 60

                                                                                  There are two main issues :
                                                                                  1 - It is not reliable enough, you can see it confused 6 000 with ele)
                                                                                  2 - it is not properly understanding where the numbers start and end, making the differentiation of the 2 columns difficult

                                                                                  I think I can solve the second issue by further splitting the image into 3 columns but I am unsure if it's not going to be a big hit on CPU / GPU usage witch I would preferably avoid

                                                                                  I also found the documentation of OpenCV that shows all of the possible Image processing methods but there is a lot and I am unsure on witch ones to use to further increase reliability

                                                                                  Any help is much appreciated


                                                                                  Answered 2022-Jan-03 at 23:02

                                                                                  Pytesseract, on its own, doesn't handle table detection very well - the table format isn't retained in the output, which can make it difficult to parse, as seen in your output.

                                                                                  So splitting the table into distinct columns, performing OCR on each, and then rejoining the columns will help. This is slower, but it is more accurate.

                                                                                  Dilation can help, which adds white pixels to existing white areas (using the threshold and image you currently have). This expands the narrow areas of the numbers.

                                                                                  In my experience, to improve the accuracy generally means splitting the table up into different sections, as well as testing different thresholds and dilation settings.

                                                                                  import cv2
                                                                                  import numpy as np
                                                                                  import pandas as pd
                                                                                  def read_img(img):
                                                                                      Read in a grayscale image.
                                                                                      img = cv2.imread(img)
                                                                                      img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
                                                                                      return img
                                                                                  img = read_img("img_path.png")
                                                                                  thresh = 255 - cv2.threshold(img, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1] # your current threshold
                                                                                  dilated = cv2.dilate(thresh, np.ones((3,1)), iterations=1) # dilate vertically (don't want to smudge the numbers together)
                                                                                  cols = []
                                                                                  for i, v in enumerate([dilated[:,0:200],thresh[:,200:500],dilated[:,800:900]]): # split image into columns by array slicing
                                                                                      # Note that the middle column isn't dilated, when so, a decimal point is found
                                                                                      config_options = '--psm 6'
                                                                                      cols.append(clean_text(pytesseract.image_to_string(v, lang='eng', config=config_options)).split('\n'))
                                                                                                     0       1    2
                                                                                  0       Aegisium Ore    4490  456
                                                                                  1        Ajatite Ore     600  332
                                                                                  2       Arkanlum Ore   84999   53
                                                                                  3        Bastium Ore    2350  421
                                                                                  4      Charodium Ore   5 280  366
                                                                                  5       Corazium Ore  39 896  212
                                                                                  6        Exorlum Ore   5 380  112
                                                                                  7                Ice     980  141
                                                                                  8    Karnite Crystal   6 000  111
                                                                                  9       Kutonlum Ore  14 000  215
                                                                                  10        Lukium Ore  31 000  158
                                                                                  11  Nhurgite Crystal    3144   64
                                                                                  12  Surtrite Crystal    4198   70
                                                                                  13       Valkite Ore     545  150
                                                                                  14      Vokarlum Ore    1850  415
                                                                                  15        Ymrium Ore  69 899   60

                                                                                  The np.ones provides a kernel for the dilation to use. Documentation.

                                                                                  Lastly, depending on your use case, AWS Textract does a good job parsing tables and numbers, and they provide sample Python code in the documentation to connect to the API, which worked really well for me, at least. Hopefully some of this is helpful.

                                                                                  Source https://stackoverflow.com/questions/70570743


                                                                                  Tesseract OCR gives really bad output even with typed text
                                                                                  Asked 2021-Dec-20 at 05:05

                                                                                  I've been trying to get tesseract OCR to extract some digits from a pre-cropped image and it's not working well at all even though the images are fairly clear. I've tried looking around for solutions but all the other questions I've seen on here involve a problem with cropping or skewed text.

                                                                                  Here's an example of my code which tries to read the image and output to the command line.

                                                                                      #convert image to greyscale for OCR
                                                                                      im_g = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
                                                                                      #create threshold image to simplify things.
                                                                                      im_t = cv2.threshold(im_g, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV)[1]
                                                                                      #define kernel size
                                                                                      rect_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20,20))
                                                                                      #Apply dilation to threshold image
                                                                                      im_d = cv2.dilate(im_t, rect_kernel, iterations = 1)
                                                                                      #Find countours
                                                                                      contours = cv2.findContours(im_t, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[0]
                                                                                      for cnt in contours:
                                                                                          x,y,w,h = cv2.boundingRect(cnt)
                                                                                          im_c = im[y:y+h, x:x+w]
                                                                                          speed = pytesseract.image_to_string(im_c)
                                                                                          print(im_path +" : " + speed)

                                                                                  Here's an example of an image

                                                                                  The output for it is:

                                                                                  frame10008.jpg : VAeVAs}

                                                                                  I've gotten a tiny improvement in some images by adding the following config to the tesseract image to string function:

                                                                                  config="--psm 7"

                                                                                  Without the new config, it would detect nothing for this image. Now it outputs

                                                                                  frame100.jpg : | U |

                                                                                  Any ideas as to what I'm doing wrong? Is there a different approach I could be taking to solve this problem? I'm open to not using Tesseract at all.


                                                                                  Answered 2021-Dec-20 at 03:04

                                                                                  I've found a decent workaround. First off I've made the image larger. More area for tesseract to work with helped it a lot. Second, to get rid of non-digit outputs, I've used the following config on the image to string function:

                                                                                  config = "--psm 7 outputbase digits"

                                                                                  That line now looks like this:

                                                                                  speed = pytesseract.image_to_string(im_c, config = "--psm 7 outputbase digits")

                                                                                  The data coming back is far from perfect but the success rate is high enough that I should be able to clean up the garbage data and interpolate where tesseract returns no digits.

                                                                                  Source https://stackoverflow.com/questions/70410527


                                                                                  Stop TensorFlow from printing warning message
                                                                                  Asked 2021-Dec-09 at 15:47

                                                                                  I am working on a Kaggle notebook and whenever I run a cell that references the TensorFlow module at all, it prints out a huge warning about some sort of settings but still works. I looked up how to suppress warnings from TensorFlow, and everything I found said to do the following:

                                                                                  import os
                                                                                  os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" # Or "3", either one should work and I've tried both

                                                                                  I have tried putting this both before and after importing TensorFlow but to no avail. The message still prints out. This is the message I am getting:

                                                                                  User settings:
                                                                                  Effective settings:
                                                                                     KMP_CPUINFO_FILE: value is not defined
                                                                                     KMP_FORCE_REDUCTION: value is not defined
                                                                                     OMP_AFFINITY_FORMAT='OMP: pid %P tid %i thread %n bound to OS proc set {%A}'
                                                                                     OMP_NESTED: deprecated; max-active-levels-var=1
                                                                                     OMP_NUM_THREADS: value is not defined
                                                                                     OMP_PLACES: value is not defined

                                                                                  Is there any way I can stop this from printing?

                                                                                  EDIT: Code to reproduce this message:

                                                                                  import tensorflow as tf

                                                                                  EDIT: Output of env:

                                                                                  {'SHELL': '/bin/bash',
                                                                                   'KMP_WARNINGS': '0',
                                                                                   'DL_ANACONDA_HOME': '/opt/conda',
                                                                                   'KAGGLE_DATA_PROXY_TOKEN': '',
                                                                                   'KAGGLE_URL_BASE': 'https://www.kaggle.com',
                                                                                   'KAGGLE_KERNEL_INTEGRATIONS': '',
                                                                                   'CONTAINER_NAME': 'tf2-cpu/2-6',
                                                                                   'PWD': '/kaggle/working',
                                                                                   'TESSERACT_PATH': '/usr/bin/tesseract',
                                                                                   'TENSORFLOW_VERSION': '2.6.0',
                                                                                   'HOME': '/root',
                                                                                   'LANG': 'C.UTF-8',
                                                                                   'KMP_SETTINGS': '1',
                                                                                   'JAX_VERSION': '0.2.19',
                                                                                   'CONTAINER_URL': 'gcr.io/deeplearning-platform-release/tf-cpu.2-6:nightly-2021-11-17',
                                                                                   'ANACONDA_PYTHON_VERSION': '3.7',
                                                                                   'PYTHONPATH': '/kaggle/lib/kagglegym:/kaggle/lib:/kaggle/input/tensorflow-great-barrier-reef',
                                                                                   'KMP_BLOCKTIME': '0',
                                                                                   'KAGGLE_DATA_PROXY_PROJECT': 'kaggle-161607',
                                                                                   'KAGGLE_USER_SECRETS_TOKEN': '',
                                                                                   'SHLVL': '1',
                                                                                   'KAGGLE_KERNEL_RUN_TYPE': 'Interactive',
                                                                                   'PROJ_LIB': '/opt/conda/share/proj',
                                                                                   'MPLBACKEND': 'agg',
                                                                                   'LD_LIBRARY_PATH': '/usr/local/cuda/lib64:/usr/local/cuda/lib:/usr/local/lib/x86_64-linux-gnu:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:',
                                                                                   'KMP_AFFINITY': 'granularity=fine,verbose,compact,1,0',
                                                                                   'MKL_THREADING_LAYER': 'GNU',
                                                                                   'LC_ALL': 'C.UTF-8',
                                                                                   'PATH': '/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin',
                                                                                   'PYTHONUSERBASE': '/root/.local',
                                                                                   'KAGGLE_DATA_PROXY_URL': 'https://dp.kaggle.net',
                                                                                   '_': '/opt/conda/bin/jupyter',
                                                                                   'GIT_PYTHON_REFRESH': 'quiet',
                                                                                   'PYDEVD_USE_FRAME_EVAL': 'NO',
                                                                                   'JPY_PARENT_PID': '9',
                                                                                   'TERM': 'xterm-color',
                                                                                   'CLICOLOR': '1',
                                                                                   'PAGER': 'cat',
                                                                                   'GIT_PAGER': 'cat',
                                                                                   'TF_CPP_MIN_LOG_LEVEL': '2',
                                                                                   'TF2_BEHAVIOR': '1'}


                                                                                  Answered 2021-Dec-09 at 15:47

                                                                                  So I managed to fix the problem with the following line:

                                                                                  os.environ["KMP_SETTINGS"] = "false"

                                                                                  Source https://stackoverflow.com/questions/70250304


                                                                                  Pytesseract image to string error messages in Colab
                                                                                  Asked 2021-Nov-23 at 15:35

                                                                                  In my Colab installed and imported pytesseract as:

                                                                                  !pip install pytesseract
                                                                                  import pytesseract
                                                                                  import cv2

                                                                                  Load the image:

                                                                                  image = cv2.imread('drive/MyDrive/test.png')

                                                                                  Then I'll get this message: (2, 'Usage: pytesseract [-l lang] input_file') if I write code as:

                                                                                  pytesseract.pytesseract.tesseract_cmd = r'/usr/local/bin/pytesseract'
                                                                                  text = pytesseract.image_to_string(image)

                                                                                  And this message: /usr/bin/tesseract is not installed or it's not in your PATH. See README file for more information. if I write:

                                                                                  pytesseract.pytesseract.tesseract_cmd = (r'/usr/bin/tesseract')
                                                                                  text = pytesseract.image_to_string(image)

                                                                                  Do you know why and how can I fix it? Please tell me if you need more information.


                                                                                  Answered 2021-Nov-23 at 15:35

                                                                                  Just be sure you've installed the underlying library the Python module is taking advantage of, for example:

                                                                                  !sudo apt install tesseract-ocr
                                                                                  # then you can do: 
                                                                                  !pip install pytesseract

                                                                                  Source https://stackoverflow.com/questions/70082929


                                                                                  tesseract detects only 4 words from image
                                                                                  Asked 2021-Nov-13 at 17:09

                                                                                  I have very simple python code:

                                                                                  import cv2
                                                                                  import pytesseract
                                                                                  pytesseract.pytesseract.tesseract_cmd = 'C:\\Tesseract-OCR\\tesseract.exe'
                                                                                  img = cv2.imread('1.png')
                                                                                  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
                                                                                  hImg,wImg,_ = img.shape
                                                                                  #detecting words
                                                                                  boxes = pytesseract.image_to_data(img)
                                                                                  for x,b in enumerate(boxes.splitlines()):
                                                                                      if x!=0:
                                                                                          b = b.split()
                                                                                          if len(b) == 12:
                                                                                              x,y,w,h = int(b[6]), int(b[7]), int(b[8]), int(b[9])
                                                                                              cv2.rectangle(img, (x,y), (w+x,h+y), (0,0,255), 3)
                                                                                  cv2.imshow('result', img)

                                                                                  But result was interesting. It detected only 4 words. what could it be the reason?


                                                                                  Answered 2021-Nov-13 at 17:09

                                                                                  You'll have better OCR results if you improve the quality of the image you are giving Tesseract.

                                                                                  While tesseract version 3.05 (and older) handle inverted image (dark background and light text) without problem, for 4.x version use dark text on light background.

                                                                                  Convert from BGR to HLS to later remove background colors from the numbers in the top half of the image. Then, create a "blue" mask with cv2.inRange and replace anything that's not "blue" with the color white.

                                                                                  # Define lower and upper limits for the number colors.
                                                                                  blue_lo=np.array([114, 70, 70])
                                                                                  blue_hi=np.array([154, 225, 225])
                                                                                  # Mask image to only select "blue"
                                                                                  # copy original image
                                                                                  img1 = img.copy()

                                                                                  Help pytesseract by converting the image to black and white

                                                                                  This is converting an image to black and white. Tesseract does this internally (Otsu algorithm), but the result can be suboptimal, particularly if the page background is of uneven darkness.

                                                                                  rgb = cv2.cvtColor(img1, cv2.COLOR_HLS2RGB)
                                                                                  gray = cv2.cvtColor(rgb, cv2.COLOR_RGB2GRAY)
                                                                                  _, img1 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

                                                                                  Use image_to_data over the previously created img1 and continue applying your existing code.

                                                                                  hImg,wImg,_ = img.shape
                                                                                  #detecting words
                                                                                  boxes = pytesseract.image_to_data(img1)
                                                                                  for x,b in enumerate(boxes.splitlines()):

                                                                                  Source https://stackoverflow.com/questions/69953632


                                                                                  How to install Tesseract OCR on Databricks
                                                                                  Asked 2021-Nov-02 at 14:08

                                                                                  I am trying to run the following script on a databrick python notebook:

                                                                                  pip install presidio-image-redactor
                                                                                  pip install pytesseract
                                                                                  python -m spacy download en_core_web_lg
                                                                                  from PIL import Image
                                                                                  from presidio_image_redactor import ImageRedactorEngine
                                                                                  import pytesseract
                                                                                  image = Image.open("images/ImageData.PNG")
                                                                                  engine = ImageRedactorEngine()
                                                                                  redacted_image = engine.redact(image, (255, 192, 203))

                                                                                  Upon running the last line, I'm getting the error below:

                                                                                  TesseractNotFoundError: tesseract is not installed or it's not in your PATH.

                                                                                  am I missing anything?


                                                                                  Answered 2021-Nov-02 at 14:08

                                                                                  You can use %sh in a separate cell to execute the shell commands on the driver node. To install tesseract, you can do:

                                                                                  %sh apt-get -f -y install tesseract-ocr 

                                                                                  If you need to install it to all nodes of the cluster, you need to use cluster init script with the same command (without %sh)

                                                                                  Source https://stackoverflow.com/questions/69807660

                                                                                  Community Discussions, Code Snippets contain sources that include Stack Exchange Network


                                                                                  No vulnerabilities reported

                                                                                  Install tesseract

                                                                                  You can download it from GitHub.


                                                                                  Before you submit an issue, please review [the guidelines for this repository](https://github.com/tesseract-ocr/tesseract/blob/main/CONTRIBUTING.md). For support, first read the [documentation](https://tesseract-ocr.github.io/tessdoc/), particularly the [FAQ](https://tesseract-ocr.github.io/tessdoc/FAQ.html) to see if your problem is addressed there. If not, search the [Tesseract user forum](https://groups.google.com/g/tesseract-ocr), the [Tesseract developer forum](https://groups.google.com/g/tesseract-dev) and [past issues](https://github.com/tesseract-ocr/tesseract/issues), and if you still can’t find what you need, ask for support in the mailing-lists. Mailing-lists: * [tesseract-ocr](https://groups.google.com/g/tesseract-ocr) - For tesseract users. * [tesseract-dev](https://groups.google.com/g/tesseract-dev) - For tesseract developers. Please report an issue only for a bug, not for asking questions.
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