# BanglaNet: Bangla Handwritten Character Recognition using Ensembling of Convolutional Neural Network

arXiv: https://arxiv.org/abs/2401.08035 · HTML: https://mlsanity.com/paper/2401.08035/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Handwritten Isolated Bangla Compound Character Recognition: a new benchmark using a novel deep learning approach](https://mlsanity.com/paper/1802.00671/) (2018)
- [An Improved Feature Descriptor for Recognition of Handwritten Bangla Alphabet](https://mlsanity.com/paper/1501.05497/) (2015)
- [Handwritten Bangla Digit Recognition Using Deep Learning](https://mlsanity.com/paper/1705.02680/) (2017)
- [Bengali Handwritten Character Classification using Transfer Learning on Deep Convolutional Neural Network](https://mlsanity.com/paper/1902.11133/) (2019)

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

- [Performance Analysis of Few-Shot Learning Approaches for Bangla Handwritten Character and Digit Recognition](https://mlsanity.com/paper/2506.00447/) (2025)

## Abstract (arXiv)

Handwritten character recognition is a crucial task because of its abundant applications. The recognition task of Bangla handwritten characters is especially challenging because of the cursive nature of Bangla characters and the presence of compound characters with more than one way of writing. In this paper, a classification model based on the ensembling of several Convolutional Neural Networks (CNN), namely, BanglaNet is proposed to classify Bangla basic characters, compound characters, numerals, and modifiers. Three different models based on the idea of state-of-the-art CNN models like Inception, ResNet, and DenseNet have been trained with both augmented and non-augmented inputs. Finally, all these models are averaged or ensembled to get the finishing model. Rigorous experimentation on three benchmark Bangla handwritten characters datasets, namely, CMATERdb, BanglaLekha-Isolated, and Ekush has exhibited significant recognition accuracies compared to some recent CNN-based research. The top-1 recognition accuracies obtained are 98.40%, 97.65%, and 97.32%, and the top-3 accuracies are 99.79%, 99.74%, and 99.56% for CMATERdb, BanglaLekha-Isolated, and Ekush datasets respectively.