Open Source Python OCR Software - Page 2

Paperless is a simple Django application running in two parts, a Consumer (the thing that does the indexing) and a Web server (the part that lets you search & download already-indexed documents). Paper is a nightmare. Environmental issues aside, there’s no excuse for it in the 21st century. It takes up space, collects dust, doesn’t support any form of a search feature, indexing is tedious, it’s heavy and prone to damage & loss. I wrote this to make “going paperless” easier. I do not have to worry about finding stuff again. I feed documents right from the post box into the scanner and then shred them. Perhaps you might find it useful too. Paperless-ng is a fork of the original paperless project. It changes many things both on the surface and under the hood. Paperless-ng was created because I feel that these changes are too big to be pushed into the main repository right away.

Turn your scanner into a free document reader for invoices (e.g. for e-banking) with the help of tesseract-ocr available for many unix (and also windows) platforms.

RadicalSpam is a free and open source package distributed under GPL v2, including products such as Postfix, SpamAssassin Amavisd-new, Clamav, Razor, DCC, Postgrey, Bind; providing a secure SMTP relay, ready to use with linux and docker environement. More information : http://www.radical-spam.org

RadicalSpam Virtual Appliance takes full solution of RadicalSpam Community Edition , pre-installed in a OVF virtual machine ( Open Virtual Format ) compatible with the best virtualization platforms on the market , including VMware ESX Server. More information : http://www.radical-spam.org

Handwriting recognition and OCR in Indic languages

Captcha Intruder is an automatic pentesting tool to bypass captchas. -> CIntruder-v0.4 (.zip) -> md5 = 6326ab514e329e4ccd5e1533d5d53967 -> CIntruder-v0.4 (.tar.gz) ->md5 = 2256fccac505064f3b84ee2c43921a68 --------------------------------------------

dots.ocr is a cutting-edge multilingual document parsing system built on a unified vision-language model that combines layout detection, text recognition, and structural understanding into a single architecture. Unlike traditional OCR pipelines that rely on multiple specialized components, dots.ocr integrates these processes end-to-end, reducing error propagation and improving consistency across tasks. The model is designed to recognize virtually any human script, making it highly effective for global and low-resource language scenarios. It achieves state-of-the-art performance on document parsing benchmarks while maintaining a relatively compact model size, demonstrating efficiency without sacrificing accuracy. Beyond standard OCR tasks, it extends its capabilities to parse complex visual elements such as charts, diagrams, and web interfaces, converting them into structured outputs like SVG code.

Software to fit whole-sentence language models using the principle of maximum entropy. For developers of speech recognizers, text prediction interfaces, OCR, machine translation software.