OCRUSREX 0.1.0

OCRUSREX takes a PDF (either by path or as a file-like object) and makes it searchable using Tesseract 4. It has an enterprise-friendly license.

What does it do?

OCRUSREX is a simple, no-frills Python 3 library to take a PDF (either by path or as a file-like object), convert it to images, run it through Tesseract 4 and return it as a PDF.

Why Another OCR Library?

There are several excellent packages out there to OCR PDFs in Python, but their licensing can be problematic for potential use in enterprise applications. Also, many of them had way more features than I needed or wanted. I just needed something to take a PDF, OCR it and give it back to me, without the overhead and potential licensing headaches of other options out there.

Install

In addition to installing the required python dependencies, you'll also need tesseract v4. You'll also want to install the appropriate training data. OCRUSREX is configured to use the best LSTM training models. You can change the default config to use fast or standard. See a description of the different Tesseract models in the Tesseract Docs:

• https://tesseract-ocr.github.io/tessdoc/Data-Files.html#updated-data-files-for-version-400-september-15-2017

Dependencies

PRODUCTION

OCRUSREX relies on three core libraries, all with MIT-like licenses:

1. pytesseract (MIT)
2. PyPDF2 (MIT-like)
3. Pillow (MIT-like)

Obviously you will need to have already installed Tesseract. Please refer to the Tesseract documentation (or pytesseract docs).

Usage

import from OCRUSREX.ocrusrex import OCRPDF OCRUSREX.OCRPDF(...options)

RETURNS: fasly if the task fails or truthy if it succeeds.

OPTIONS:

• source = ""

  • What is the target PDF to be OCRed? This can be a String containing a valid path to a pdf or a file-like object.

• targetPath = None

  • Where should the OCRed PDF be saved? If you don't provide a value, the odf will be returned as a byte object.

• page = None

  • If you provide an integer value, only a single page will be OCRed and returned. If you leave this as None The entire PDF will be OCRed.

• nice = 5

  • Sets the priority of the thread in Unix-like operating systems. 5 is slightly elevated.

• verbose = False

  • If this is set to True, show messages in the console.

• tesseract_config = '--oem 1 --psm 6 -l best/eng -c preserve_interword_spaces=1'

  • This will be passed to pytesseract as the config option. These are the command line arguments you would pass directly to tesseract were you calling it directly. The defaults here are optimized for accuracy but require you download an additional tesseract data file (the "best" one). This comes at the expense of speed.

TESTS

The tests also rely on:

1. python-levenshtein
2. tika-python.

Neither are required for the core library to work.

FUTURE

This could be multithreaded, potentially, or, as it splits the PDF up first and then OCRs every page separately, it could esaily be scaled horizontally, particularly if you use the OCRPDF_page_tobytesobj object call.

The use-case I have in mind will be deployed on an asychronous, cloud-based framework. I plan to split my OCR jobs into multiple calls, each with a single page, and run each one in its own Celery task-runner, so multithreading directly inside of OCRUSREX isn't a required feature for me. I can see why it would be very helpful for local usage, however, so, if you know Python multithreading, please feel free to contribute!