kba / awesome-ocr Goto Github PK
View Code? Open in Web Editor NEWLinks to awesome OCR projects
Home Page: https://github.com/kba/awesome-ocr
License: Creative Commons Zero v1.0 Universal
Links to awesome OCR projects
Home Page: https://github.com/kba/awesome-ocr
License: Creative Commons Zero v1.0 Universal
Lios is a free and open source software for converting print in to text using either scanner or a camera, It can also produce text out of scanned images from other sources such as Pdf, Image, Folder containing Images or screenshot. Program is given total accessibility for visually impaired. Lios is written in python3, and we release it under GPL-3 license. There are great many possibilities for this program, Feedback is the key to it, Expecting your feedback.
Active development, looks really cool, screenshots: https://sourceforge.net/projects/lios/
has some facilities for training tesseract.
Source code: https://gitlab.com/Nalin-x-Linux/lios-3
https://github.com/tmbdev/ocropy/wiki/Publications
Are they awesome enough? ๐
ocropus - OCR engine based on CLSTM, Apache 2.0
It's the other way around. 'ocropy' was out before 'clstm' :-)
Maybe the intention was 'LSTM' and not 'CLSTM'.
Using 'ocropus' and linking to ocropy might confuse anyone that does not know ocropy's history.
https://www.nr.no/~eikvil/OCR.pdf
Recently, the use of neural networks to recognize characters (and other types of patterns)
has resurfaced. Considering a back-propagation network, this network is composed of
several layers of interconnected elements. A feature vector enters the network at the input
layer. Each element of the layer computes a weighted sum of its input and transforms it
into an output by a nonlinear function. During training the weights at each connection are
adjusted until a desired output is obtained. A problem of neural networks in OCR may be
their limited predictability and generality, while an advantage is their adaptive nature.
In Future improvements
Integration of segmentation and contextual analysis can improve recognition of joined
and split characters. Also, higher level contextual analysis which look at the semantics of
entire sentences may be useful. Generally there is a potential in using context to a larger
extent than what is done today. In addition, combinations of multiple independent feature
sets and classifiers, where the weakness of one method is compensated by the strength of
another, may improve the recognition of individual characters
And it seems that OCR for printed types has been solved for a solid 23 years ๐
The frontiers of research within character recognition have now moved towards the rec-
ognition of cursive script, that is handwritten connected or calligraphic characters.
There are some nice python scripts for various image pre-processing tasks described at https://mzucker.github.io/ eg "Unprojecting text with ellipses", "Compressing and enhancing hand-written notes", and "Page dewarping". The blog posts contain links to the scripts themselves in the author's GitHub repository.
Written by Ray Smith and other googlers.
https://arxiv.org/abs/1702.03970
http://rrc.cvc.uab.es/?ch=6
https://github.com/tensorflow/models/tree/master/street
It would be tremendously useful to have a variety of real-life OCR results and ground-truth.
@suub has some here https://github.com/suub/ocr-engine-results/tree/master/demo/ground-truth
Banti Telugu OCR: https://github.com/TeluguOCR/banti_telugu_ocr
"This framework relies on the ability of a segmentation algorithm to break the text in to glyphs."
Chamanti OCR: https://github.com/rakeshvar/chamanti_ocr
"It will not rely on segmentation algorithms (at the glyph level), making it ideal for highly agglutinative scripts like Arabic, Devanagari etc. We will be starting with Telugu however."
It is hard to guess for me, how good the recognition work, because I don't understand Telugu and I haven't found any results, discussions, blog posts (which I can read). But the project looks IMO very promising from a technical point. CC @rakeshvar
Initially made for sanitizing whiteboard photos, but it should be applicable to other images:
Also, these mysterious Docker containers by gb17@dockerhub: ubuntu-kafka-ocr and ubuntu-kafka-abby.
Mentioned in #13, cont'd here:
The synth 90k text recognition dataset is probably a useful addition to the list.
Also of interest: https://github.com/edward-zhu/umaru
https://github.com/michal-h21/hocrtex
This hasn't been updated in a while but it's a really cool approach to creating PDFs from OCR data.
I suggest to add https://github.com/tokee/quack by @tokee to the list.
@inproceedings{nagy1999optical,
title={Optical character recognition: An illustrated guide to the frontier},
author={Nagy, George and Nartker, Thomas A and Rice, Stephen V},
booktitle={Electronic Imaging},
pages={58--69},
year={1999},
organization={International Society for Optics and Photonics}
}
Plumb a PDF for detailed information about each char, rectangle, line, et cetera โ and easily extract text and tables.
Seems really useful to reverse engineer layout information.
@inproceedings{pletschacher2015europeana,
title={Europeana newspapers OCR workflow evaluation},
author={Pletschacher, Stefan and Clausner, Christian and Antonacopoulos, Apostolos},
booktitle={Proceedings of the 3rd International Workshop on Historical Document Imaging and Processing},
pages={39--46},
year={2015},
organization={ACM}
}
hocr-inkscape integration?
https://github.com/urieli/jochre
Jochre is an OCR package based on supervised machine learning techniques. It has been applied to several languages, including Yiddish, Occitan and Alsacien.
Fascinating stuff: http://www.nature.com/ncomms/2016/160909/ncomms12665/full/ncomms12665.html
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.