pyocr | Simple python application that uses a BP neural network | Machine Learning library

I would like to extract text from scanned PDFs.
My "test" code is as follows:

My objective is to use OCR in Python 2.7 using Tesseract on a Windows 7 machine, but I am running into issues as for the installation process. I tried following the instruction here but the link to "tesseract-core-yyyymmdd.exe" and "tesseract-langs-yyyymmdd.exe" do not exist anymore and I can't find these .exe elsewhere online. Here's what I have done so far:

1. installed tesseract from its executable from official tesseract-ocr page.
2. installed via pip packages "wand", "PIL", "pyocr".

Now, if I do the following in Python:

from wand.image import Image from PIL import Image as PI import pyocr import pyocr.builders import io

No problem loading up these packages but pyocr.get_available_tools() gives me an empty list. I am sure this has to do with the missing installation .exe files above. Where can I find them? Is it something else that I am missing?

So, from what I can begin..

I am working with OCR. The script works pretty well for what I need. It detects the words with an accuracy which for me is ok.

This is the result: 100% accuracy with attached image.

When I read in a multi page Tiff Image which is 15 pages and is a document in black letters/words in white background ,PyTesseract throws an "OSError: -9" error at the step where I loop over the pages and convert to string.

I use the pytesseract package along with pyocr.builders. The single page seem to work fine but I believe the error when the image is not in RGB the program converts to RGB.

I have a PDF file consisting of around 20-25 pages. The aim of this tool is to split the PDF file into pages (using PyPdf2), save every PDF page in a directory (using PyPdf2), convert the PDF pages into images (using ImageMagick) and then perform some OCR on them using tesseract (using PIL and PyOCR) to extract data. The tool will eventually be a GUI through tkinter so the users can perform the same operation many times by clicking on a button. Throughout my heavy testing, I have noticed that if the whole process is repeated around 6-7 times, the tool/python script crashes by showing not responding on Windows. I have performed some debugging, but unfortunately there is no error thrown. The memory and CPU are good so no issues there as well. I was able to narrow down the problem by observing that, before reaching to the tesseract part, PyPDF2 and ImageMagick are failing when they are run together. I was able to replicate the problem by simplifying it to the following Python code:

I have downloaded Mayan EDMS-Electronic Document Management System from GitHub and I configured project using Django server. I had added the required libraries based on requirement. Now the project runs with error

I am using pytessaract to do an image to text conversion of a numberplate for something like this

I believe this is my first StackOverflow question, so please be nice.

I am OCRing a repository of PDFs (~1GB in total) ranging from 50-200 pages each and found that suddenly all of the available 100GB of remaining harddrive space on my Macbook Pro were gone. Based on a previous post, it seems that ImageMagick is the culprit as shown here.

I found that these files are called 'magick-*' and are stored in /private/var/tmp. For only 23 PDFs it had created 3576 files totaling 181GB.

How can I delete these files immediately within the code after they are no longer needed? Thank you in advance for any suggestions to remedy this issue.

Here is the code:

I've been testing text recognition from images using pyocr (tesseract-ocr and libetesseract). I've been applying various PIL.ImageFilters and getting the result of one specific string in the image. It has not been accurate, but I have 14 different results. Between all of them, all of the correct letters of the string in the image are there. So I have enumerated each string and created a dict containing the characters' position as keys that contain a dict of each character that has appeared in that position at keys and the number of occurrences as the value. Here's a shortened example