bnhocr

Optical Character Recognition for bangla handwritten and printed documents

Single Grapheme Prediction based model, Bangla HOCR

   version:0.0.1
   authors:MD.Nazmuddoha Ansary, (team ovijatrik,apsis solutions ltd,bengali.ai)
           MD.Mobassir Hossain,  (team ovijatrik,apsis solutions ltd,bengali.ai) 
           MD.Aminul Islam       (team ovijatrik)

This project was created in association with:

• Apsis Solutions LTD
• Bengali.ai

Environment

• For ubuntu install tesseract bangla: install tesseract-ocr-ben
• For windows (Untested source):
  • Download and install tesseract-ocr-w64-setup-v5.0.0-rc1.20211030.exe (or the latest one)
  • Open https://github.com/tesseract-ocr/tessdata and download your language. For example, for Bangla download ben.traineddata.
  • Copy the downloaded file to the tessreact_ocr installation location, some location like: C:\Program Files\Tesseract-OCR\tessdata
  • Don't forget to use the traineddata name for the language. For bangla, I use lang='ben'.

python requirements

• pip requirements: pip install -r requirements.txt

Its better to use a virtual environment Some of the pip requirements may not work properly due to locally saved modules OR use conda-

• Preffered way: conda: use environment.yml: conda env create -f environment.yml

model requirements

• Download model.h5 file
• place the model.h5 file under models folder

LOCAL ENVIRONMENT/TESTING ENVIRONMENT

OS          : Ubuntu 20.04.3 LTS       
Memory      : 23.4 GiB 
Processor   : Intel® Core™ i5-8250U CPU @ 1.60GHz × 8    
Graphics    : Intel® UHD Graphics 620 (Kabylake GT2)  
Gnome       : 3.36.8

About bnhocr: Printed and Handwritten text recognition

There are available models such as : tesseract,Easy OCR. that covers bangla printed text to a considerable accuracy.In this project we solely focus on handwritten texts.

• The idea of bnhocr project is to convert handwritten graphemes to a unique representational space (in our example font faced image).
• Converting single graphemes does not cover words. Separated graphemes can be used if a grapheme localization model is used.Watch This Video To For The Basic Idea

• To extend for words, we build on single grapheme transformation model and extend on handwritten words.
• Then we use an existing recognizer (in our example-tesseract)

• A short presentation about this work is available at resources/slides.pdf

Demo

• clone the repo
• install dependencies
• streamlit run app.py

Graphemes

• To understand the concept of bangla graphemes: A large multi-target dataset of common bengali handwritten graphemes
• Cite the paper if you find it useful in your research

@inproceedings{alam2021large,
  title={A large multi-target dataset of common bengali handwritten graphemes},
  author={Alam, Samiul and Reasat, Tahsin and Sushmit, Asif Shahriyar and Siddique, Sadi Mohammad and Rahman, Fuad and Hasan, Mahady and Humayun, Ahmed Imtiaz},
  booktitle={International Conference on Document Analysis and Recognition},
  pages={383--398},
  year={2021},
  organization={Springer}
}

Known Issues

• model is not cached while running in streamlit
• only launched for tesseract
  • Easy OCR and Detection models can be easily added for wide applications