Accuracy improvement for handwritten Japanese word recognition by combination of character and word recognizer

Hotta, Yuji; Takebe, Hiroaki; Suwa, Misako; Naoi, Satoshi

doi:10.1109/icdar.2005.41

Eighth International Conference on Document Analysis and Recognition (ICDAR'05) 2005

DOI: 10.1109/icdar.2005.41

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Accuracy improvement for handwritten Japanese word recognition by combination of character and word recognizer

Yuji Hotta

Hiroaki Takebe

Misako Suwa

et al.

Abstract: This paper proposes a combination method of character recognizer (CR) and word recognizer (WR) for Japanese handwritten words. CR is composed of character segmentation, isolated character recognition, and postprocessing. On the other hand, WR recognizes word images holistically based on a given word lexicon. These methods have been used as key modules in handwritten address recognition but have not been compared quantitatively up to now. In this paper, CR and WR are first empirically compared with each other u… Show more

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Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields

Zhou

Wang²,

Tian³

et al. 2013

IEEE Trans. Pattern Anal. Mach. Intell.

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This paper proposes a method for handwritten Chinese/Japanese text (character string) recognition based on semi-Markov conditional random fields (semi-CRFs). The high-order semi-CRF model is defined on a lattice containing all possible segmentation-recognition hypotheses of a string to elegantly fuse the scores of candidate character recognition and the compatibilities of geometric and linguistic contexts by representing them in the feature functions. Based on given models of character recognition and compatibilities, the fusion parameters are optimized by minimizing the negative log-likelihood loss with a margin term on a training string sample set. A forward-backward lattice pruning algorithm is proposed to reduce the computation in training when trigram language models are used, and beam search techniques are investigated to accelerate the decoding speed. We evaluate the performance of the proposed method on unconstrained online handwritten text lines of three databases. On the test sets of databases CASIA-OLHWDB (Chinese) and TUAT Kondate (Japanese), the character level correct rates are 95.20 and 95.44 percent, and the accurate rates are 94.54 and 94.55 percent, respectively. On the test set (online handwritten texts) of ICDAR 2011 Chinese handwriting recognition competition, the proposed method outperforms the best system in competition.

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Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields

Zhou

Wang²,

Tian³

et al. 2013

IEEE Trans. Pattern Anal. Mach. Intell.

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Online Handwritten Gurmukhi Words Recognition

Singh

Sharma

2019

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Identification of offline and online handwritten words is a challenging and complex task. In comparison to Latin and Oriental scripts, the research and study of handwriting recognition at word level in Indic scripts is at its initial phases. The two main methods of handwriting recognition are global and analytical. The present work introduces a novel analytical approach for online handwritten Gurmukhi word recognition based on a minimal set of words and recognizes an input Gurmukhi word as a sequence of characters. We employed a sequential step-by-step approach to recognize online handwritten Gurmukhi words. Considering the massive variability in online Gurmukhi handwriting, the present work employs the completely linked non-homogeneous hidden Markov model. In the present study, we considered the dependent, major-dependent, and super-dependent nature of strokes to form Gurmukhi characters in words. On test sets of online handwritten Gurmukhi datasets, the word-level accuracy rates are 85.98%, 84.80%, 82.40%, and 82.20% in four different modes. Besides the online Gurmukhi word recognition, the present work also provides Gurmukhi handwriting analysis study for varying writing styles and proposes novel techniques for zone detection and rearrangement of strokes. Our proposed algorithms have been successfully employed to online handwritten Gurmukhi word recognition in dependent and independent modes of handwriting.

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Accuracy improvement for handwritten Japanese word recognition by combination of character and word recognizer

Cited by 2 publications

References 4 publications

Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields

Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields

Online Handwritten Gurmukhi Words Recognition

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