Personal Dictionaries for Handwritten Character Recognition Using Characters Written by a Similar Writer

Tsuruoka, Shuichi; Hattori, Masahiro; Kadir, Mohd Fadzil bin Abdul; Takano, Toshiaki; Kawanaka, Hiroharu; Takase, Haruhiko; Miyake, Yoichi

doi:10.1109/icfhr.2010.98

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…The Fixed Length Segmentation Matching provides an effective way to solve the uncharted error character string matching with the dictionary phrase. The core of this method is the characters' matching is not relying on the conversion of pending segmentation character strings [4]. It can use the uncharted error character to compare with the dictionary directly and get the intro-code which we can use it in the Glyph Code Matrix Transforming to get the pending alteration strings' character in the dictionary.…”

Section: A Fixed Length Segmentation Matching Algorithmmentioning

confidence: 99%

An OCR Post-Processing Method Based on Dictionary Matching and Matrix Transforming

Guo

Tang

Zhang

et al. 2013

AMM

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This paper describes a post-processing method for Chinese and Japanese character recognition based on dictionary. By the analysis results of recognition in the processing of OCR, we can find some segmentation and recognition errors do not conform to the rules of lexical and just recognized as the characters which its fonts approach to the scanned texts. For these errors we can deal with them by the Fix Length Segmentation Matching based on Dictionary and the Glyph Code Matrix Transforming. Through the above processing, most of the inaccurate recognitions can be corrected and by the experimental results, it can be proved that this method is an effective way to improve the recognition rate of Chinese and Japanese Character.

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Section: A Fixed Length Segmentation Matching Algorithmmentioning

confidence: 99%

An OCR Post-Processing Method Based on Dictionary Matching and Matrix Transforming

Guo

Tang

Zhang

et al. 2013

AMM

View full text Add to dashboard Cite

show abstract

“…The mixture (1) approaches personal mean using only one character per category, and it is effective for the improvement of recognition rate. To resolve the writing cost of a specific writer in the above mentioned discussion, we proposed two new generating methods of adaptive dictionary, especially mixture type dictionary, using only one character for all categories [9]. The key idea is the usage of characters written by the similar writer registered in advance.…”

Section: Effect Of One Character Per Categorymentioning

confidence: 99%

“…The personal adaptive dictionary is generates using the characters written by the similar writer and many writers. We proposed two types, that is, "Similar mean dictionary", "Similar feature space dictionary" [9]. We compared two proposed types for Japanese character "Hiragana" at offline.…”

Section: Introductionmentioning

confidence: 99%