Common Sense Knowledge for Handwritten Chinese Text Recognition

Wang, Qiu-Feng; Cambria, Erik; Liu, Cheng-Lin; Hussain, Amir

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.

Citation

Wang, Q.-F., Cambria, E., Liu, C.-L., & Hussain, A. (2013). Common Sense Knowledge for Handwritten Chinese Text Recognition. Cognitive Computation, 5(2), 234-242. https://doi.org/10.1007/s12559-012-9183-y

• Journal Article Type: Article
• Acceptance Date: Aug 9, 2012
• Online Publication Date: Aug 23, 2012
• Publication Date: 2013-06
• Deposit Date: Oct 11, 2019
• Journal: Cognitive Computation
• Print ISSN: 1866-9956
• Electronic ISSN: 1866-9964
• Publisher: BMC
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 2
• Pages: 234-242
• DOI: https://doi.org/10.1007/s12559-012-9183-y
• Keywords: Common sense knowledge; Natural language processing; Linguistic context; n-gram; Handwritten Chinese text recognition
• Public URL: http://researchrepository.napier.ac.uk/Output/1793121