# Handwritten Word Recognition using Deep Learning Approach: A Novel Way of Generating Handwritten Words

arXiv: https://arxiv.org/abs/2303.07514 · HTML: https://mlsanity.com/paper/2303.07514/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [BanglaWriting: A multi-purpose offline Bangla handwriting dataset](https://mlsanity.com/paper/2011.07499/) (2020)
- [Implicit Language Model in LSTM for OCR](https://mlsanity.com/paper/1805.09441/) (2018)
- [LILA-BOTI : Leveraging Isolated Letter Accumulations By Ordering Teacher Insights for Bangla Handwriting Recognition](https://mlsanity.com/paper/2205.11420/) (2022)

## Built on by (most cited first, 5 of 5)

- [Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis](https://mlsanity.com/paper/2305.19593/) (2023)
- [Autism Disease Detection Using Transfer Learning Techniques: Performance Comparison Between Central Processing Unit vs Graphics Processing Unit Functions for Neural Networks](https://mlsanity.com/paper/2306.00283/) (2023)
- [Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection](https://mlsanity.com/paper/2306.00284/) (2023)
- [Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks](https://mlsanity.com/paper/2306.07981/) (2023)
- [A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data](https://mlsanity.com/paper/2308.09722/) (2023)

## Abstract (arXiv)

A handwritten word recognition system comes with issues such as lack of large and diverse datasets. It is necessary to resolve such issues since millions of official documents can be digitized by training deep learning models using a large and diverse dataset. Due to the lack of data availability, the trained model does not give the expected result. Thus, it has a high chance of showing poor results. This paper proposes a novel way of generating diverse handwritten word images using handwritten characters. The idea of our project is to train the BiLSTM-CTC architecture with generated synthetic handwritten words. The whole approach shows the process of generating two types of large and diverse handwritten word datasets: overlapped and non-overlapped. Since handwritten words also have issues like overlapping between two characters, we have tried to put it into our experimental part. We have also demonstrated the process of recognizing handwritten documents using the deep learning model. For the experiments, we have targeted the Bangla language, which lacks the handwritten word dataset, and can be followed for any language. Our approach is less complex and less costly than traditional GAN models. Finally, we have evaluated our model using Word Error Rate (WER), accuracy, f1-score, precision, and recall metrics. The model gives 39% WER score, 92% percent accuracy, and 92% percent f1 scores using non-overlapped data and 63% percent WER score, 83% percent accuracy, and 85% percent f1 scores using overlapped data.