tesseract | CSS-Framework based on existing software principles | Application Framework library
kandi X-RAY | tesseract Summary
kandi X-RAY | tesseract Summary
→ Reference page containing all components and modules. It’s a challenge in any web development project to structure and organize all the css code. Existing css frameworks often disregard basic software principles. This makes the use of them difficult and your own software even more difficult to manage. This css framework is build upon existing software principles to help keeping things simple and manageable.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of tesseract
tesseract Key Features
tesseract Examples and Code Snippets
Lines of Code : 39
License : No License
Copybower install git@github.com:spape/tesseract.git
gem 'sass'
gem 'sass-globbing'
gem 'compass-rails'
# config/application.rb
config.assets.paths << Rails.root.join('bower_components', 'tesseract', 'src', 'sass')
@import 'tesseract'
.button-s .font-size-xxs {
font-size: 70%;
}
.font-size-xs {
font-size: 90%;
}
.font-size-s {
font-size: 100%;
}
.font-size-m {
font-size: 110%;
}
.font-size-l {
font-size: 140%;
}
.font-size-xl {
font-size: 160%;
}
.font-size-xxl {
font-si .border-top {
border-top: 1px solid grey;
}
.border-right {
border-right: 1px solid grey;
}
.border-bottom {
border-bottom: 1px solid grey;
}
.border-left {
border-left: 1px solid grey;
}
.border-all {
border-top: 1px solid grey;
border-r Community Discussions
Trending Discussions on tesseract
QUESTION
I would like to extract the definitions from the book The Navajo Language: A Grammar and Colloquial Dictionary by Young and Morgan. They look like this (very blurry):
I tried running it through the Google Cloud Vision API, and got decent results, but it doesn't know what to do with these "special" letters with accent marks on them, or the curls and lines on/through them. And because of the blurryness (there are no alternative sources of the PDF), it gets a lot of them wrong. So I'm thinking of doing it from scratch in Tesseract. Note the term is bold and the definition is not bold.
How can I use Node.js and Tesseract to get basically an array of JSON objects sort of like this:
...ANSWER
Answered 2021-Jun-15 at 20:17Tesseract takes a lang variable that you can expand to include different languages if they're installed. I've used the UB Mannheim (https://github.com/UB-Mannheim/tesseract/wiki) installation which includes a ton of languages supported.
To get better and more accurate results, the best thing to do is to process the image before handing it to Tesseract. Set a white/black threshold so that you have black text on white background with no shading. I'm not sure how to do this in Node, but I've done it with Python's OpenCV library.
If that font doesn't get you decent results with the out of the box, then you'll want to train your own, yes. This blog post walks through the process in great detail: https://towardsdatascience.com/simple-ocr-with-tesseract-a4341e4564b6. It revolves around using the jTessBoxEditor to hand-label the objects detected in the images you're using.
Edit: In brief, the process to train your own:
- Install jTessBoxEditor (https://sourceforge.net/projects/vietocr/files/jTessBoxEditor/). Requires Java Runtime installed as well.
- Collect your training images. They want to be .tiffs. I found I got fairly accurate results with not a whole lot of images that had a good sample of all the characters I wanted to detect. Maybe 30/40 images. It's tedious, so you don't want to do TOO many, but need enough in order to get a good sampling.
- Use jTessBoxEditor to merge all the images into a single .tiff
- Create a training label file (.box)j. This is done with Tesseract itself.
tesseract your_language.font.exp0.tif your_language.font.exp0 makebox - Now you can open the box file in jTessBoxEditor and you'll see how/where it detected the characters. Bounding boxes and what character it saw. The tedious part: Hand fix all the bounding boxes and characters to accurately represent what is in the images. Not joking, it's tedious. Slap some tv episodes up and just churn through it.
- Train the tesseract model itself
- save a file:
font_propertieswho's content isfont 0 0 0 0 0 - run the following commands:
tesseract num.font.exp0.tif font_name.font.exp0 nobatch box.train
unicharset_extractor font_name.font.exp0.box
shapeclustering -F font_properties -U unicharset -O font_name.unicharset font_name.font.exp0.tr
mftraining -F font_properties -U unicharset -O font_name.unicharset font_name.font.exp0.tr
cntraining font_name.font.exp0.tr
You should, in there close to the end see some output that looks like this:
Master shape_table:Number of shapes = 10 max unichars = 1 number with multiple unichars = 0
That number of shapes should roughly be the number of characters present in all the image files you've provided.
If it went well, you should have 4 files created: inttemp normproto pffmtable shapetable. Rename them all with the prefix of your_language from before. So e.g. your_language.inttemp etc.
Then run:
combine_tessdata your_language
The file: your_language.traineddata is the model. Copy that into your Tesseract's data folder. On Windows, it'll be like: C:\Program Files x86\tesseract\4.0\tessdata and on Linux it's probably something like /usr/shared/tesseract/4.0/tessdata.
Then when you run Tesseract, you'll pass the lang=your_language. I found best results when I still passed an existing language as well, so like for my stuff it was still English I was grabbing, just funny fonts. So I still wanted the English as well, so I'd pass: lang=your_language+eng.
QUESTION
I am trying to extract Hindi text from a PDF. I tried all the methods to exract from the PDF, but none of them worked. There are explanations why it doesn't work, but no answers as such. So, I decided to convert the PDF to an image, and then use pytesseract to extract texts. I have downloaded the Hindi trained data, however that also gives highly inaccurate text.
That's the actual Hindi text from the PDF (download link):
That's my code so far:
...ANSWER
Answered 2021-Jun-08 at 14:46It seems the module pdfplumber does the work:
QUESTION
I have some sketched images where the images contain text captions. I am trying to remove those caption.
I am using this code:
...ANSWER
Answered 2021-Jun-09 at 20:15The cv2 pre-processing is unecessary here, tesseract is able to find the text on its own. See the example below, commented inline:
QUESTION
I am trying to generate a visual studio 2019 C++ project from the tesseract 4.1.1 source code. Ultimately, I want to include a tesseract C++ project in my custom solution that consumes OCR results.
When I follow these steps:
- Download and extract tesseract code https://github.com/tesseract-ocr/tesseract/archive/refs/tags/4.1.1.zip to "C:\tesseract" directory.
- Execute the following commands in a Developer Command Prompt for VS 2019:
C:\Windows\System32>cd "C:\tesseract"
C:\tesseract>mkdir build
C:\tesseract>cd build
C:\tesseract\build>cmake ..
I receive this error:
...ANSWER
Answered 2021-Jun-05 at 07:13There are several tutorial how to build tesseract on windows with cmake and VS e.g. https://bucket401.blogspot.com/2021/03/building-tesserocr-on-ms-windows-64bit.html (you can ignore end of tutorial - python module), minimalist tesseract or with clang
QUESTION
ANSWER
Answered 2021-Jun-04 at 19:55Improving the quality of the output is your "holy scripture" when working with Tesseract. Especially, the page segmentation method should always be explicitly set. Here (as most of the times), I'd opt for --psm 6:
Assume a single uniform block of text.
Even without further preprocessing of your image, you already get the desired result:
QUESTION
ANSWER
Answered 2021-May-26 at 21:04From the top to bottom. The boxes are approximately at (x1, y1, x2, y2)
- 0.2564, 0.1070, 0.6293, 0.166
- 0.2377, 0.6826, 0.7645, 0.703
- 0.331, 0.88, 0.6713, 0.913
In relative to width and height. The full code would be like
QUESTION
I am trying to detect the .pdf file text.
They are first converted to an image, then given to Tesseract.
The detection is good but they make too many line breaks.
For example if the file is a bit panched on the right, the sentence:
"I like Tesseract for reading text"
become:
"text read for Tesseract like I"
And that's already after a treatment because the raw text is :
"text
read
for
Tesseract
like
I"
The bug occurs since the source .pdf are in 300DPI, I understand that the problem comes from the resolution but I cannot find how to solve it.
Here is my Tesseract cmd Tesseract.exe dummy.pdf dumy-ocr.pdf --psm 12 --dpi 300 -l bvr+fra+eng+deu hocr pdf
First, I would like to solve the problem of too many lines,
Then I would find out how to make the image perfectly straight
Thank you in advance for your help
ANSWER
Answered 2021-May-26 at 21:19You seem to be working backwards. The "many" lines and thus word reversal are due to the anti-clockwise rotation.
QUESTION
I am using the latest Tesseract API for C++ and I followed the last answer on this post to link what is necessary. These are my includes:
...ANSWER
Answered 2021-May-17 at 01:51Try compiling it with VS2019. The recent builds of Tesseract were built with VS2019.
QUESTION
I am developing a web application which has image processing functions. So I used opencv-python and implemented the python script to node js using python-shell package,
index.js;
...ANSWER
Answered 2021-May-16 at 17:24I solved the error by giving the full path of the image in the python script to imread()
QUESTION
I am getting this error when I try to use pytesseract in colab.
I am not sure how to fix this problem. I also install with pip install tesseract. But it doesn't work.
Does anyone know how to solve this issue? Or do you have any other python library OCR?
...ANSWER
Answered 2021-May-09 at 06:28This code will work in colab in-case pytesseract is not installed.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install tesseract
or alternatively grunt serve. Builds the framework in /.tmp, spawns a webserver, opens the reference page and provides livereload. Builds the framework and a compressed version in /dist and opens the reference page. Afterwards it creates screenshots from the reference page (overview) and screenshots for each module/component. Those screenshots are used to compare/identify visiual changes (see (Regression Testing)[#regression-testing]).
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