Volume: 15, Issue: 2(2002)
pp. 117-131 DOI: 10.1142/S0219427902000571
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Abstract |
Full Text (PDF, 117KB)
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| Title: |
Handwritten Japanese Character Recognition Using Adaptive Shape Normalization by
Global Affine Transformation |
| Author(s): |
TORU WAKAHARA
Faculty of Computer and Information Sciences,
Hosei University, 3-7-2 Kajino-cho, Koganei-shi, Tokyo,
184-8584, Japan
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| Abstract: |
This paper proposes a new, promising character recognition system with a
category-dependent shape normalization technique that normalizes an input pattern
against each reference pattern adaptively using global affine transformation (GAT) as
follows. (1) An input character pattern is fed to "the basic OCR", the
most powerful of the conventional OCRs. (2) The basic OCR plays the role of rough
classification and outputs a small set of candidate categories for the input pattern.
(3) GAT normalizes the input pattern against each candidate's reference pattern
adaptively. (4) Each adaptively normalized input pattern is fed again to the basic
OCR. (5) The final recognition result is obtained using the updated
"distance" values within candidate categories. In experiments, our basic OCR
linked to GAT adaptive normalization is successfully applied to 28,694 patterns of
totally unconstrained handwritten characters, including Kanji, Kana, and alphanumerics,
written by 300 people, with substantial improvements in recognition accuracy. |
| Keywords: |
Adaptive Shape Normalization; Japanese Handwritten Character Recognition; Global Affine Transformation; Weighted Least-Squares Criterion; Successive Iteration Method
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