research-article

Handwritten Manipuri Meetei-Mayek Classification Using Convolutional Neural Network

Authors:

Kishorjit Nongmeikapam,

Kanan Wahengbam,

Oinam Nickson Meetei,

Themrichon TuithungAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 18, Issue 4

Article No.: 35, Pages 1 - 23

https://doi.org/10.1145/3309497

Published: 07 May 2019 Publication History

Abstract

A new technique for classifying all 56 different characters of the Manipuri Meetei-Mayek (MMM) is proposed herein. The characters are grouped under five categories, which are Eeyek Eepee (original alphabets), Lom Eeyek (additional letters), Cheising Eeyek (digits), Lonsum Eeyek (letters with short endings), and Cheitap Eeyek (vowel signs. Two related works proposed by previous researchers are studied for understanding the benefits claimed by the proposed deep learning approach in handwritten Manipuri Meetei-Mayek. (1) Histogram of Oriented (HOG) with SVM classifier is implemented for thoroughly understanding how HOG features can influence accuracy. (2) The handwritten samples are trained using simple Convolutional Neural Network (CNN) and compared with the proposed CNN-based architecture. Significant progress has been made in the field of Optical Character Recognition (OCR) for well-known Indian languages as well as globally popular languages. Our work is novel in the sense that there is no record of work available to date that is able to classify all 56 classes of the MMM. It will also serve as a pre-cursor for developing end-to-end OCR software for translating old manuscripts, newspaper archives, books, and so on.

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Cited By

Hijam DSaharia S(2024)A multilevel recognition of Meitei Mayek handwritten characters using fusion of features strategyThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02776-340:1(211-225)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s00371-023-02776-3
Handique BDas P(2024)A Review in Assamese Handwritten Character RecognitionProceedings of the NIELIT's International Conference on Communication, Electronics and Digital Technology10.1007/978-981-97-3601-0_38(527-549)Online publication date: 31-Jul-2024
https://doi.org/10.1007/978-981-97-3601-0_38
Islam MIacob I(2023)Manuscripts Character Recognition Using Machine Learning and Deep LearningModelling10.3390/modelling40200104:2(168-188)Online publication date: 4-Apr-2023
https://doi.org/10.3390/modelling4020010
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Index Terms

Handwritten Manipuri Meetei-Mayek Classification Using Convolutional Neural Network
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees

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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 18, Issue 4

December 2019

305 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3327969

Editor:
Nianwen Xue
Brandeis University, Waltham, USA

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Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 May 2019

Accepted: 01 January 2019

Revised: 01 December 2018

Received: 01 August 2017

Published in TALLIP Volume 18, Issue 4

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Cited By

Hijam DSaharia S(2024)A multilevel recognition of Meitei Mayek handwritten characters using fusion of features strategyThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02776-340:1(211-225)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s00371-023-02776-3
Handique BDas P(2024)A Review in Assamese Handwritten Character RecognitionProceedings of the NIELIT's International Conference on Communication, Electronics and Digital Technology10.1007/978-981-97-3601-0_38(527-549)Online publication date: 31-Jul-2024
https://doi.org/10.1007/978-981-97-3601-0_38
Islam MIacob I(2023)Manuscripts Character Recognition Using Machine Learning and Deep LearningModelling10.3390/modelling40200104:2(168-188)Online publication date: 4-Apr-2023
https://doi.org/10.3390/modelling4020010
Li ZXu YZhao NZhou YLiu YLin DHe S(2023)Parsing-Conditioned Anime Translation: A New Dataset and MethodACM Transactions on Graphics10.1145/358500242:3(1-14)Online publication date: 10-Apr-2023
https://dl.acm.org/doi/10.1145/3585002
Dey RBalabantaray R(2023)Development of a Benchmark Odia Handwritten Character Database for an Efficient Offline Handwritten Character Recognition with a Chronological SurveyACM Transactions on Asian and Low-Resource Language Information Processing10.1145/358398822:6(1-28)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1145/3583988
Dai HZhao T(2022)Intelligent Analysis Strategy of Pragmatic Failure in Cross-Cultural Communication Based on Convolution Neural NetworkMobile Information Systems10.1155/2022/78039592022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7803959
Guo HDong NZhao JLiu Y(2022)Handwritten New Tai Lue Character Recognition Using Convolutional Prior Features and Deep Variationally Sparse Gaussian Process ModelingACM Transactions on Asian and Low-Resource Language Information Processing10.1145/350670021:4(1-25)Online publication date: 20-Jan-2022
https://dl.acm.org/doi/10.1145/3506700
Mahajan SRani R(2022)Word Level Script Identification Using Convolutional Neural Network Enhancement for Scenic ImagesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/350669921:4(1-29)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3506699
Chanu P(2022) Manipuri Handwritten Script Recognition Using Machine and Deep Learning Machine Learning Algorithms for Signal and Image Processing10.1002/9781119861850.ch8(129-137)Online publication date: 18-Nov-2022
https://doi.org/10.1002/9781119861850.ch8
Prasanna DTripathi S(2022)Machine and Deep‐Learning Techniques for Text and Speech ProcessingMachine Learning Algorithms for Signal and Image Processing10.1002/9781119861850.ch7(115-128)Online publication date: 18-Nov-2022
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