research-article

Semantic and Context Understanding for Sentiment Analysis in Hindi Handwritten Character Recognition Using a Multiresolution Technique

Authors:

Mohammad R. Khosravi,

Parul AgarwalAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 23, Issue 1

Article No.: 5, Pages 1 - 22

https://doi.org/10.1145/3557895

Published: 15 January 2024 Publication History

Abstract

The rapid growth of Web 2.0, which enables people to generate, communicate, and share information, has resulted in an increase in the total number of users. In developing countries, online users’ sentiment influences decision-making, social views, individual consumption decisions, and entity quality monitoring. As a result, more accurate sentiment analysis, particularly in their native language such as Hindi, is preferred over crude binary categorization. This is because of the abundance of web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. Analyzing this data and recovering valuable and relevant information from handwritten text has become extremely important. Despite years of research and development, no optical writing recognition (OCR) system has ever been certified as completely reliable. The first step in any pattern recognition system is feature selection. In many fields, feature selection is studied as a combinatorial optimization problem. The primary goal of feature selection is to reduce the number of redundant and ineffective traits in the recognition system. This feature selection is used to maintain or improve the performance of the classifier used by the recognition system: A support vector machine (SVM) technique could be used to solve this character recognition problem. The Hindi character recognition system recognizes Hindi characters by employing morphological operations, edge detection, HOG feature extraction, and an SVM-based classifier. The proposed model outperformed the current state-of-the-art method, achieving an accuracy of 96.77%.

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Cited By

Kukade JPawar RRai IPatidar MJain KPandit D(2024)Decoding the Devanagari - Handwritten Hindi Recognition Using Deep Learning Methodology2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)10.1109/ACROSET62108.2024.10743214(1-6)Online publication date: 27-Sep-2024
https://doi.org/10.1109/ACROSET62108.2024.10743214
Ma TDu XWu XZhou ZZheng YJin C(2023)Reading Scene Text with Aggregated Temporal Convolutional EncoderACM Transactions on Asian and Low-Resource Language Information Processing10.1145/362582222:11(1-16)Online publication date: 12-Oct-2023
https://dl.acm.org/doi/10.1145/3625822
Singh KPandey SYadav DSingh TKumar GKumar A(2023)Deep Learning: The Future of Medical Image Processing2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES)10.1109/CISES58720.2023.10183477(699-704)Online publication date: 28-Apr-2023
https://doi.org/10.1109/CISES58720.2023.10183477

Index Terms

Semantic and Context Understanding for Sentiment Analysis in Hindi Handwritten Character Recognition Using a Multiresolution Technique
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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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 23, Issue 1

January 2024

385 pages

EISSN:2375-4702

DOI:10.1145/3613498

Editor:
Imed Zitoun
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 ACM 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: 15 January 2024

Online AM: 06 October 2022

Accepted: 21 July 2022

Revised: 30 June 2022

Received: 30 May 2022

Published in TALLIP Volume 23, Issue 1

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Cited By

Kukade JPawar RRai IPatidar MJain KPandit D(2024)Decoding the Devanagari - Handwritten Hindi Recognition Using Deep Learning Methodology2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)10.1109/ACROSET62108.2024.10743214(1-6)Online publication date: 27-Sep-2024
https://doi.org/10.1109/ACROSET62108.2024.10743214
Ma TDu XWu XZhou ZZheng YJin C(2023)Reading Scene Text with Aggregated Temporal Convolutional EncoderACM Transactions on Asian and Low-Resource Language Information Processing10.1145/362582222:11(1-16)Online publication date: 12-Oct-2023
https://dl.acm.org/doi/10.1145/3625822
Singh KPandey SYadav DSingh TKumar GKumar A(2023)Deep Learning: The Future of Medical Image Processing2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES)10.1109/CISES58720.2023.10183477(699-704)Online publication date: 28-Apr-2023
https://doi.org/10.1109/CISES58720.2023.10183477

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