# BN-DRISHTI: Bangla Document Recognition through Instance-level Segmentation of Handwritten Text Images

arXiv: https://arxiv.org/abs/2306.09351 · HTML: https://mlsanity.com/paper/2306.09351/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 4 of 4)

- [Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition](https://mlsanity.com/paper/1604.08352/) (2016)
- [Text Line Segmentation for Challenging Handwritten Document Images Using Fully Convolutional Network](https://mlsanity.com/paper/2101.08299/) (2021)
- [BanglaWriting: A multi-purpose offline Bangla handwriting dataset](https://mlsanity.com/paper/2011.07499/) (2020)
- [BN-HTRd: A Benchmark Dataset for Document Level Offline Bangla Handwritten Text Recognition (HTR) and Line Segmentation](https://mlsanity.com/paper/2206.08977/) (2022)

## Built on by (most cited first, 1 of 1)

- [GraDeT-HTR: A Resource-Efficient Bengali Handwritten Text Recognition System utilizing Grapheme-based Tokenizer and Decoder-only Transformer](https://mlsanity.com/paper/2509.18081/) (2025)

## Abstract (arXiv)

Handwriting recognition remains challenging for some of the most spoken languages, like Bangla, due to the complexity of line and word segmentation brought by the curvilinear nature of writing and lack of quality datasets. This paper solves the segmentation problem by introducing a state-of-the-art method (BN-DRISHTI) that combines a deep learning-based object detection framework (YOLO) with Hough and Affine transformation for skew correction. However, training deep learning models requires a massive amount of data. Thus, we also present an extended version of the BN-HTRd dataset comprising 786 full-page handwritten Bangla document images, line and word-level annotation for segmentation, and corresponding ground truths for word recognition. Evaluation on the test portion of our dataset resulted in an F-score of 99.97% for line and 98% for word segmentation. For comparative analysis, we used three external Bangla handwritten datasets, namely BanglaWriting, WBSUBNdb_text, and ICDAR 2013, where our system outperformed by a significant margin, further justifying the performance of our approach on completely unseen samples.