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Cognitive Computation

Erschienen in:

01.06.2013

Common Sense Knowledge for Handwritten Chinese Text Recognition

verfasst von: Qiu-Feng Wang, Erik Cambria, Cheng-Lin Liu, Amir Hussain

Erschienen in: Cognitive Computation | Ausgabe 2/2013

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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.

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In the case of unconstrained texts, no corpus is wide enough to contain all possible n-grams.

In Chinese, a word can comprises one or multiple characters, which can explore both syntactic and semantic meaning better than a character.

High-order n-gram models need much larger training corpus and higher cost of computation and memory, n usually takes no more than 5 in practice.

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Titel: Common Sense Knowledge for Handwritten Chinese Text Recognition
verfasst von: Qiu-Feng Wang
Erik Cambria
Cheng-Lin Liu
Amir Hussain
Publikationsdatum: 01.06.2013
Verlag: Springer-Verlag
Erschienen in: Cognitive Computation / Ausgabe 2/2013
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-012-9183-y

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