Online Mongolian Handwriting Recognition Based on Encoder–Decoder Structure with Language Model

Mongolian online handwriting recognition is a complex task due to the script’s intricate characters and extensive vocabulary. This study proposes a novel approach by integrating a pre-trained language model into the sequence-to-sequence(Seq2Seq) + attention mechanisms(AM) model to enhance recognitio...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 12; no. 20; p. 4194
Main Authors: Fan, Daoerji, Sun, Yuxin, Wang, Zhixin, Peng, Yanjun
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2023
Subjects:
Accuracy
Algorithms
Computational linguistics
Cultural heritage
Datasets
Handwriting
Handwriting recognition
Language
Language processing
Model accuracy
Mongolian language
Natural language interfaces
Neural networks
Object recognition (Computers)
Pattern recognition
Penmanship
Voice recognition
Writing
Mongolia
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:Mongolian online handwriting recognition is a complex task due to the script’s intricate characters and extensive vocabulary. This study proposes a novel approach by integrating a pre-trained language model into the sequence-to-sequence(Seq2Seq) + attention mechanisms(AM) model to enhance recognition accuracy. Three fusion models, including former, latter, and complete fusion, are introduced, showing substantial improvements over the baseline model. The complete fusion model, combined with synchronized language model parameters, achieved the best results, significantly reducing character and word error rates. This research presents a promising solution for accurate Mongolian online handwriting recognition, offering practical applications in preserving and utilizing the Mongolian script.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12204194