Affine-Invariant Recognition of Gray-Scale Characters Using Global Affine Transformation Correlation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Writer Adaptation for Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline Handwritten Chinese Character Recognition Using Optimal Sampling Features
ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
Analysis and Recognition of Asian Scripts - the State of the Art
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Video text recognition using feature compensation as category-dependent feature extraction
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A new method for important points extraction
CompSysTech '04 Proceedings of the 5th international conference on Computer systems and technologies
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Conventional normalization methods for handwritten characters have limitations as preprocessing operations because they are category-independent. This paper introduces an adaptive or category-dependent normalization method that normalizes an input pattern against each reference pattern using global/local affine transformation (GAT/LAT) in a hierarchical manner as a general deformation model. Also, the normalization criterion is clearly defined as minimization of the mean of nearest-neighbor interpoint distances between each reference pattern and a normalized input pattern. According to the above-mentioned criterion, optimal GAT/LAT is determined by iterative application of weighted least-squares fitting techniques. Experiments using input patterns of 3,171 character categories, including Kanji, Kana, and alphanumerics, written by 36 people in the cursive style against square-style reference patterns show not only that the proposed method can absorb a fairly large amount of handwriting fluctuation within the same category, but also that discrimination ability is greatly improved by the suppression of excessive normalization against similarly shaped but different categories. Furthermore, comparative results obtained by the conventional shape normalization method for preprocessing are presented to show the superiority of the proposed category-dependent GAT/LAT normalization over category-independent normalization.