# Urdu Handwritten Text Recognition Using ResNet18

arXiv: https://arxiv.org/abs/2103.05105 · HTML: https://mlsanity.com/paper/2103.05105/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 31 references that are in the map.
- First posted on arXiv 19 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network](https://mlsanity.com/paper/1912.07943/) (2019)

## Built on by (most cited first, 2 of 2)

- [UTRNet: High-Resolution Urdu Text Recognition In Printed Documents](https://mlsanity.com/paper/2306.15782/) (2023)
- [Urdu Katib Handwritten Dataset: A Historical Document Dataset for Offline Urdu Handwritten Text Recognition with CRNN-Based Baseline Evaluation](https://mlsanity.com/paper/2606.19139/) (2026)

## Abstract (arXiv)

Handwritten text recognition is an active research area in the field of deep learning and artificial intelligence to convert handwritten text into machine-understandable. A lot of work has been done for other languages, especially for English, but work for the Urdu language is very minimal due to the cursive nature of Urdu characters. The need for Urdu HCR systems is increasing because of the advancement of technology. In this paper, we propose a ResNet18 model for handwritten text recognition using Urdu Nastaliq Handwritten Dataset (UNHD) which contains 3,12000 words written by 500 candidates.