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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Common Sense Knowledge for Handwritten Chinese Text Recognition
Authors: Wang, Qiu-Feng
Cambria, Erik
Liu, Cheng-Lin
Hussain, Amir
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Keywords: Common sense knowledge
Natural language processing
Linguistic context
Handwritten Chinese text recognition
Issue Date: Jun-2013
Publisher: Springer
Citation: Wang Q, Cambria E, Liu C & Hussain A (2013) Common Sense Knowledge for Handwritten Chinese Text Recognition, Cognitive Computation, 5 (2), pp. 234-242.
Abstract: Compared to human intelligence, computers are far short of common sense knowledge which people normally acquire during the formative years of their lives. This paper investigates the effects of employing common sense knowledge as a new linguistic context in handwritten Chinese text recognition. Three methods are introduced to supplement the standard n-gram language model: embedding model, direct model, and an ensemble of these two. The embedding model uses semantic similarities from common sense knowledge to make the n-gram probabilities estimation more reliable, especially for the unseen n-grams in the training text corpus. The direct model, in turn, considers the linguistic context of the whole document to make up for the short context limit of the n-gram model. The three models are evaluated on a large unconstrained handwriting database, CASIA-HWDB, and the results show that the adoption of common sense knowledge yields improvements in recognition performance, despite the reduced concept list hereby employed.
Type: Journal Article
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Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: Chinese Academy of Sciences
National University of Singapore
Chinese Academy of Sciences
Computing Science - CSM Dept

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