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Development of a Benchmark Odia Handwritten Character Database for an Efficient Offline Handwritten Character Recognition with a Chronological Survey

Authors:

Rakesh Chandra BalabantarayAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 22, Issue 6

Article No.: 155, Pages 1 - 28

https://doi.org/10.1145/3583988

Published: 17 June 2023 Publication History

Abstract

A good benchmark dataset is a primary requirement in the offline handwritten character recognition (HCR) process. Only three handwritten numerals and alphabet datasets from Odia are publicly accessible for study, although many writers have used several datasets in their experiments. In this article, two tasks are done to address this issue. Those are the following: First, an extensive survey focused on various datasets is provided with the methodologies used in chronological order. The second factor is a solution to the lack of publicly available handwritten characters and numeral datasets. A new dataset of handwritten Odia characters with numerals has been developed. Anyone can access this dataset by sending an email to the authors of the article. This dataset was created with the help of 150 volunteers of various age groups, races, and qualifications. Some homogeneous experiments are conducted using deep learning models to evaluate the consistency of the dataset. One heterogeneous trial has also been performed to estimate the complexities of the characters present in the dataset by comparing them with the existing benchmark datasets.

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Cited By

Raquib MHossain MIslam MMiah M(2024)VashaNet: An automated system for recognizing handwritten Bangla basic characters using deep convolutional neural networkMachine Learning with Applications10.1016/j.mlwa.2024.10056817(100568)Online publication date: Sep-2024
https://doi.org/10.1016/j.mlwa.2024.100568
Dey RPiri JBehera DKhan A(2023)A time efficient offline handwritten character recognition using convolutional extreme learning machineThe Imaging Science Journal10.1080/13682199.2023.222301172:6(736-748)Online publication date: 24-Jun-2023
https://doi.org/10.1080/13682199.2023.2223011

Index Terms

Development of a Benchmark Odia Handwritten Character Database for an Efficient Offline Handwritten Character Recognition with a Chronological Survey
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

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Information & Contributors

Information

Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 22, Issue 6

June 2023

635 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3604597

Editor:
Imed Zitouni
Google, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 June 2023

Online AM: 21 February 2023

Accepted: 03 February 2023

Revised: 31 October 2022

Received: 14 April 2021

Published in TALLIP Volume 22, Issue 6

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Cited By

Raquib MHossain MIslam MMiah M(2024)VashaNet: An automated system for recognizing handwritten Bangla basic characters using deep convolutional neural networkMachine Learning with Applications10.1016/j.mlwa.2024.10056817(100568)Online publication date: Sep-2024
https://doi.org/10.1016/j.mlwa.2024.100568
Dey RPiri JBehera DKhan A(2023)A time efficient offline handwritten character recognition using convolutional extreme learning machineThe Imaging Science Journal10.1080/13682199.2023.222301172:6(736-748)Online publication date: 24-Jun-2023
https://doi.org/10.1080/13682199.2023.2223011

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