# A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes

arXiv: https://arxiv.org/abs/2010.00170 · HTML: https://mlsanity.com/paper/2010.00170/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 12 of 12)

- [BaDLAD: A Large Multi-Domain Bengali Document Layout Analysis Dataset](https://mlsanity.com/paper/2303.05325/) (2023)
- [OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking](https://mlsanity.com/paper/2305.09688/) (2023)
- [Script Fragmentation and Format: What Drives the English-Bengali Performance Gap in Open LLMs?](https://mlsanity.com/paper/2507.23248/) (2025)
- [Bengali Common Voice Speech Dataset for Automatic Speech Recognition](https://mlsanity.com/paper/2206.14053/) (2022)
- [Real-time Bangla License Plate Recognition System for Low Resource Video-based Applications](https://mlsanity.com/paper/2108.08339/) (2021)
- [Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models](https://mlsanity.com/paper/2209.12650/) (2022)
- [PLATTER: A Page-Level Handwritten Text Recognition System for Indic Scripts](https://mlsanity.com/paper/2502.06172/) (2025)
- [Unicode Normalization and Grapheme Parsing of Indic Languages](https://mlsanity.com/paper/2306.01743/) (2023)
- [BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection on Bangla Clickbait Dataset](https://mlsanity.com/paper/2311.06204/) (2023)
- [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)
- [Bengali Handwritten Grapheme Classification: Deep Learning Approach](https://mlsanity.com/paper/2111.08249/) (2021)
- [Bengali Document Layout Analysis -- A YOLOV8 Based Ensembling Approach](https://mlsanity.com/paper/2309.00848/) (2023)

## Abstract (arXiv)

Latin has historically led the state-of-the-art in handwritten optical character recognition (OCR) research. Adapting existing systems from Latin to alpha-syllabary languages is particularly challenging due to a sharp contrast between their orthographies. The segmentation of graphical constituents corresponding to characters becomes significantly hard due to a cursive writing system and frequent use of diacritics in the alpha-syllabary family of languages. We propose a labeling scheme based on graphemes (linguistic segments of word formation) that makes segmentation in-side alpha-syllabary words linear and present the first dataset of Bengali handwritten graphemes that are commonly used in an everyday context. The dataset contains 411k curated samples of 1295 unique commonly used Bengali graphemes. Additionally, the test set contains 900 uncommon Bengali graphemes for out of dictionary performance evaluation. The dataset is open-sourced as a part of a public Handwritten Grapheme Classification Challenge on Kaggle to benchmark vision algorithms for multi-target grapheme classification. The unique graphemes present in this dataset are selected based on commonality in the Google Bengali ASR corpus. From competition proceedings, we see that deep-learning methods can generalize to a large span of out of dictionary graphemes which are absent during training. Dataset and starter codes at www.kaggle.com/c/bengaliai-cv19.