# HCR-Net: A deep learning based script independent handwritten character recognition network

arXiv: https://arxiv.org/abs/2108.06663 · HTML: https://mlsanity.com/paper/2108.06663/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 126 references that are in the map.
- First posted on arXiv 15 Aug 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (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)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Kannada-MNIST: A new handwritten digits dataset for the Kannada language](https://mlsanity.com/paper/1908.01242/) (2019)
- [Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks](https://mlsanity.com/paper/1905.05928/) (2019)
- [Handwritten Isolated Bangla Compound Character Recognition: a new benchmark using a novel deep learning approach](https://mlsanity.com/paper/1802.00671/) (2018)
- [Building Efficient CNN Architecture for Offline Handwritten Chinese Character Recognition](https://mlsanity.com/paper/1804.01259/) (2018)
- [Persian Handwritten Digit, Character and Word Recognition Using Deep Learning](https://mlsanity.com/paper/2010.12880/) (2020)
- [An Improved Feature Descriptor for Recognition of Handwritten Bangla Alphabet](https://mlsanity.com/paper/1501.05497/) (2015)
- [Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network](https://mlsanity.com/paper/1912.07943/) (2019)
- [Handwritten Bangla Digit Recognition Using Deep Learning](https://mlsanity.com/paper/1705.02680/) (2017)
- [MNIST-MIX: A Multi-language Handwritten Digit Recognition Dataset](https://mlsanity.com/paper/2004.03848/) (2020)
- [BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks](https://mlsanity.com/paper/1906.03786/) (2019)
- [Stroke-Based Cursive Character Recognition](https://mlsanity.com/paper/1304.0421/) (2013)
- [A High-Performance CNN Method for Offline Handwritten Chinese Character Recognition and Visualization](https://mlsanity.com/paper/1812.11489/) (2018)

## Built on by (most cited first, 7 of 7)

- [A Brief Review of Hypernetworks in Deep Learning](https://mlsanity.com/paper/2306.06955/) (2023)
- [Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation](https://mlsanity.com/paper/2305.15984/) (2023)
- [Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes](https://mlsanity.com/paper/2502.08282/) (2025)
- [Automatic Intermodal Loading Unit Identification using Computer Vision: A Scoping Review](https://mlsanity.com/paper/2509.17707/) (2025)
- [Handwritten Text Recognition for Low Resource Languages](https://mlsanity.com/paper/2512.01348/) (2025)
- [BornoViT: A Novel Efficient Vision Transformer for Bengali Handwritten Basic Characters Classification](https://mlsanity.com/paper/2603.00755/) (2026)
- [Rethinking Handwritten Character Recognition](https://mlsanity.com/paper/2609.10572/) (2026)

## Abstract (arXiv)

Handwritten character recognition (HCR) remains a challenging pattern recognition problem despite decades of research, and lacks research on script independent recognition techniques. colorblackThis is mainly because of similar character structures, different handwriting styles, diverse scripts, handcrafted feature extraction techniques, unavailability of data and code, and the development of script-specific deep learning techniques. To address these limitations, we have proposed a script independent deep learning network for HCR research, called HCR-Net, that sets a new research direction for the field. HCR-Net is based on a novel transfer learning approach for HCR, which textitpartly utilizes feature extraction layers of a pre-trained network. Due to transfer learning and image augmentation, HCR-Net provides faster and computationally efficient training, better performance and generalizations, and can work with small datasets. HCR-Net is extensively evaluated on 40 publicly available datasets of Bangla, Punjabi, Hindi, English, Swedish, Urdu, Farsi, Tibetan, Kannada, Malayalam, Telugu, Marathi, Nepali and Arabic languages, and established 26 new benchmark results while performed close to the best results in the rest cases. HCR-Net showed performance improvements up to 11% against the existing results and achieved a fast convergence rate showing up to 99% of final performance in the very first epoch. HCR-Net significantly outperformed the state-of-the-art transfer learning techniques and also reduced the number of trainable parameters by 34% as compared with the corresponding pre-trained network. To facilitate reproducibility and further advancements of HCR research, the complete code is publicly released at urlhttps://github.com/jmdvinodjmd/HCR-Net.