# Enhancement of Bengali OCR by Specialized Models and Advanced Techniques for Diverse Document Types

arXiv: https://arxiv.org/abs/2402.05158 · HTML: https://mlsanity.com/paper/2402.05158/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [Complicated Table Structure Recognition](https://mlsanity.com/paper/1908.04729/) (2019)
- [DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts](https://mlsanity.com/paper/2202.01414/) (2022)
- [Open Evaluation Tool for Layout Analysis of Document Images](https://mlsanity.com/paper/1712.01656/) (2017)
- [MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters](https://mlsanity.com/paper/2005.02155/) (2020)
- [DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding](https://mlsanity.com/paper/2207.06695/) (2022)

## Abstract (arXiv)

This research paper presents a unique Bengali OCR system with some capabilities. The system excels in reconstructing document layouts while preserving structure, alignment, and images. It incorporates advanced image and signature detection for accurate extraction. Specialized models for word segmentation cater to diverse document types, including computer-composed, letterpress, typewriter, and handwritten documents. The system handles static and dynamic handwritten inputs, recognizing various writing styles. Furthermore, it has the ability to recognize compound characters in Bengali. Extensive data collection efforts provide a diverse corpus, while advanced technical components optimize character and word recognition. Additional contributions include image, logo, signature and table recognition, perspective correction, layout reconstruction, and a queuing module for efficient and scalable processing. The system demonstrates outstanding performance in efficient and accurate text extraction and analysis.