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Bridging the Gap between Handwriting and Machine: AI-based Handwritten Text Recognition

by Dhruv Shah, Sai Bhargav, Dhanush B.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 7
Year of Publication: 2023
Authors: Dhruv Shah, Sai Bhargav, Dhanush B.
10.5120/ijca2023922727

Dhruv Shah, Sai Bhargav, Dhanush B. . Bridging the Gap between Handwriting and Machine: AI-based Handwritten Text Recognition. International Journal of Computer Applications. 185, 7 ( May 2023), 39-45. DOI=10.5120/ijca2023922727

@article{ 10.5120/ijca2023922727,
author = { Dhruv Shah, Sai Bhargav, Dhanush B. },
title = { Bridging the Gap between Handwriting and Machine: AI-based Handwritten Text Recognition },
journal = { International Journal of Computer Applications },
issue_date = { May 2023 },
volume = { 185 },
number = { 7 },
month = { May },
year = { 2023 },
issn = { 0975-8887 },
pages = { 39-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number7/32717-2023922727/ },
doi = { 10.5120/ijca2023922727 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:25:32.123965+05:30
%A Dhruv Shah
%A Sai Bhargav
%A Dhanush B.
%T Bridging the Gap between Handwriting and Machine: AI-based Handwritten Text Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 7
%P 39-45
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Due to the variety and complexity of handwriting styles, handwritten text recognition (HTR) is a difficult job. In HTR, artificial intelligence (AI) has demonstrated enormous promise, opening the door to the creation of effective and precise identification systems. An overview of the HTR process using AI methods, including preprocessing of the data, feature extraction, classification, and post-processing, is provided in this paper. To get the input data ready for feature extraction, data preprocessing includes image enhancement and segmentation. To feed the classification model with useful features, such as character shapes and patterns, pertinent features are extracted from the input picture. A neural network is frequently used in the classification model to map the extracted features to the associated characters. Spell checkers and language models are two post-processing tools that can be used to improve recognition outcomes. We discuss the challenges and opportunities for HTR using Neural Networks, including the importance of training data, model selection, and performance evaluation. The paper also outlines the methodology, design, and architecture of the Handwriting character recognition system and the testing and results of the system development. The aim is to demonstrate the effectiveness of neural networks for Handwriting character recognition.

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Index Terms

Computer Science
Information Sciences

Keywords

Handwriting Text Recognition Artificial Intelligence Machine Learning Artificial Neural Networks Image Processing Support Vector Machine Computer Vision.

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