research-article

Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition

Author:

Cheng-Lin LiuAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 29, Issue 8

Pages 1465 - 1469

https://doi.org/10.1109/TPAMI.2007.1090

Published: 01 August 2007 Publication History

Abstract

The gradient direction histogram feature has shown superior performance in character recognition. To alleviate the effect of stroke direction distortion caused by shape normalization and provide higher recognition accuracies, we propose a new feature extraction approach, called normalization-cooperated gradient feature (NCGF) extraction, which maps the gradient direction elements of original image to direction planes without generating the normalized image and can be combined with various normalization methods. Experiments on handwritten Japanese and Chinese character databases show that, compared to normalization-based gradient feature, the NCGF reduces the recognition error rate by factors ranging from 8.63 percent to 14.97 percent with high confidence of significance when combined with pseudo-two-dimensional normalization.

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Cited By

Sharma DParashar VParashar A(2024)Identification of Personality Traits by Machine Learning Analysis of Signatures and HandwritingSN Computer Science10.1007/s42979-024-02798-15:5Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1007/s42979-024-02798-1
Li M(2023)Handwritten Chinese Character Recognition Based on Residual Neural NetworkProceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering10.1145/3650400.3650686(1715-1719)Online publication date: 20-Oct-2023
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Index Terms

Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition
1. Computing methodologies

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Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 29, Issue 8

August 2007

191 pages

ISSN:0162-8828

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Copyright © 2007.

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IEEE Computer Society

United States

Publication History

Published: 01 August 2007

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Cited By

Sharma DParashar VParashar A(2024)Identification of Personality Traits by Machine Learning Analysis of Signatures and HandwritingSN Computer Science10.1007/s42979-024-02798-15:5Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1007/s42979-024-02798-1
Li M(2023)Handwritten Chinese Character Recognition Based on Residual Neural NetworkProceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering10.1145/3650400.3650686(1715-1719)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1145/3650400.3650686
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Yan KGuo JZhou W(2021)A Novel Method for Offline Handwritten Chinese Character Recognition Under the Guidance of PrintAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-75765-6_9(106-117)Online publication date: 11-May-2021
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Li ZTeng NJin MLu H(2018)Building efficient CNN architecture for offline handwritten Chinese character recognitionInternational Journal on Document Analysis and Recognition10.5555/3288248.328825821:4(233-240)Online publication date: 1-Dec-2018
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