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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Vydáno v:Electronics (Basel) Ročník 12; číslo 20; s. 4194
Hlavní autoři: Fan, Daoerji, Sun, Yuxin, Wang, Zhixin, Peng, Yanjun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.10.2023
Témata:
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
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Abstract 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.
AbstractList 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.
Audience Academic
Author Wang, Zhixin
Sun, Yuxin
Fan, Daoerji
Peng, Yanjun
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SubjectTerms 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
Title Online Mongolian Handwriting Recognition Based on Encoder–Decoder Structure with Language Model
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