Nonlinear shape normalization methods for the recognition of large-set handwritten characters☆
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Cited by (83)
A bibliometric analysis of off-line handwritten document analysis literature (1990–2020)
2022, Pattern RecognitionA prototype classification method and its use in a hybrid solution for multiclass pattern recognition
2006, Pattern RecognitionCitation Excerpt :The second group consists of two large-scale classification tasks, in which we use full sets of ETL8B (comprised of 956 character types) and ETL9B (comprised of 3036 character types). For all data sets, we use a feature extraction method that consists of three basic techniques [9,19,20]: non-linear normalization [21,22], directional feature extraction [9,20], and feature blurring [23]. According to Umeda [24], these techniques represent major breakthroughs in handwritten Chinese/Hiragana character recognition.
Pseudo two-dimensional shape normalization methods for handwritten Chinese character recognition
2005, Pattern RecognitionCitation Excerpt :The moment normalization method of Casey [1] and the geometric projection method of Nagy and Tuong [2] were proposed for normalizing alphanumeric characters. For normalizing Chinese characters that contain multiple strokes, the nonlinear normalization (NLN) method based on line density equalization is proven very efficient [3]. The line density can be computed in various ways yet the methods of Tsukumo and Tanaka [4] and Yamada et al. [5] have been widely adopted.
An improved handwritten Chinese character recognition system using support vector machine
2005, Pattern Recognition LettersCitation Excerpt :Lee and Park (1994) compared the performance and computational complexity of existing nonlinear normalization methods and experimental results indicated that Yamada et al.’s method based on line density (Yamada et al., 1990) performed better while having a higher computational cost and side effects of zigzags.
The stroke correspondence problem, revisited
2019, arXivBenchmark dataset for offline handwritten character recognition
2017, Proceedings - 2017 13th International Conference on Emerging Technologies, ICET2017
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A preliminary version of this paper has been presented at the 2nd International Conference on Document Analysis and Recognition, Tsukuba Science City, Japan, October 1993.