# An Improved Feature Descriptor for Recognition of Handwritten Bangla Alphabet

arXiv: https://arxiv.org/abs/1501.05497 · HTML: https://mlsanity.com/paper/1501.05497/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2015.

## Built on by (most cited first, 4 of 4)

- [HCR-Net: A deep learning based script independent handwritten character recognition network](https://mlsanity.com/paper/2108.06663/) (2021)
- [uTHCD: A New Benchmarking for Tamil Handwritten OCR](https://mlsanity.com/paper/2103.07676/) (2021)
- [BanglaNet: Bangla Handwritten Character Recognition using Ensembling of Convolutional Neural Network](https://mlsanity.com/paper/2401.08035/) (2024)
- [Optical Text Recognition in Nepali and Bengali: A Transformer-based Approach](https://mlsanity.com/paper/2404.02375/) (2024)

## Abstract (arXiv)

Appropriate feature set for representation of pattern classes is one of the most important aspects of handwritten character recognition. The effectiveness of features depends on the discriminating power of the features chosen to represent patterns of different classes. However, discriminatory features are not easily measurable. Investigative experimentation is necessary for identifying discriminatory features. In the present work we have identified a new variation of feature set which significantly outperforms on handwritten Bangla alphabet from the previously used feature set. 132 number of features in all viz. modified shadow features, octant and centroid features, distance based features, quad tree based longest run features are used here. Using this feature set the recognition performance increases sharply from the 75.05% observed in our previous work [7], to 85.40% on 50 character classes with MLP based classifier on the same dataset.