On-line overlaid-handwriting recognition based on substroke HMMs

Shimodaira, Hiroshi; Sudo, Takashi; Nakai, Masaaki; Sagayama, Shigeki

doi:10.1109/icdar.2003.1227816

Cited by 15 publications

(8 citation statements)

References 6 publications

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“…For the Japanese character, Shimodaira et al [5] proposed a system that can recognize overlapped kanji characters. It seems to be more convenient to input kanji directly.…”

Section: Comparisons With Prior Workmentioning

confidence: 99%

Text Input System Using Online Overlapped Handwriting Recognition for Mobile Devices

Tonouchi

Kawamura

2007

Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2

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This paper proposes a novel online overlapped handwriting recognition system for mobile devices such as cellular phones. Users can input characters continuously without pauses on the single writing area. It has three features: small writing area, quick response and direct operations with handwritten gestures. Therefore, it is suitable for mobile devices such as cellular phones. The system realizes a new handwriting interface similar to touch-typing. We evaluated the system by two experiments: character recognition performance and text entry speed of Japanese sentences. Through these experiments we showed the effectiveness of the proposed system.

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“…For the Japanese character, Shimodaira et al [5] proposed a system that can recognize overlapped kanji characters. It seems to be more convenient to input kanji directly.…”

Section: Comparisons With Prior Workmentioning

confidence: 99%

Text Input System Using Online Overlapped Handwriting Recognition for Mobile Devices

Tonouchi

Kawamura

2007

Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2

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“…Shimodaira et al [7] introduced substroke Hidden Markov Models (HMMs) with a bigram language model for overlaid handwritten Japanese text. Using a bigram model consisting of 1,016 Japanese educational Kanji and 71 Hiragana characters, the character recognition rates are 74.9% for free stroke order patterns and 91.1% for fixed stroke order patterns.…”

Section: Copyright Cmentioning

confidence: 99%

Character-Position-Free On-Line Handwritten Japanese Text Recognition by Two Segmentation Methods

Liang

Zhu

Kumagai

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARY The paper presents a recognition method of characterposition-free on-line handwritten Japanese text patterns to allow a user to overlay characters freely without confirming previously written characters. To develop this method, we first collected text patterns written without wrist or elbow support and without visual feedback and then prepared large sets of character-position-free handwritten Japanese text patterns artificially from normally handwritten text patterns. The proposed method sets each off-stroke between real strokes as undecided and evaluates the segmentation probability by SVM model. Then, the optimal segmentationrecognition path can be effectively found by Viterbi search in the candidate lattice, combining the scores of character recognition, geometric features, linguistic context, as well as the segmentation scores by SVM classification. We test this method on variously overlaid sample patterns, as well as on the above-mentioned collected handwritten patterns, and verify that its recognition rates match those of the latest recognizer for normally handwritten horizontal Japanese text with no serious speed restriction in practical applications.

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“…In [5], Shimodaira et al introduced a substroke HMM based method for overlapped Japanese character recognition. It can recognize 1016 Japanese educational Kanji and 71 Hiragana characters, and the recognition rate is about 69.2% when different stroke order was permitted.…”

Section: Introductionmentioning

confidence: 99%

Overlapped Handwriting Input on Mobile Phones

Zou

Liu

et al. 2011

2011 International Conference on Document Analysis and Recognition

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In this paper, we propose an overlapped handwriting input method on handheld devices, which allows users to write continuously without breaks on a single size-restricted writing area. 2 issues have been considered during the implementation of the overlapped input method: previous characters on the background may obstruct the clear viewing of current character and the messy overlapped handwriting is difficult to be segmented and recognized. In our method, a quick segmentation method based on an artificial neural network is used to tackle the first problem and a novel system is implemented to recognize the messy handwriting based on the output of an isolated character recognition engine and a language model. The recognition rate for Chinese characters is about 92.5% for a testing database containing GB2312 Chinese characters and other frequently used symbols. The positive feedbacks from testers have also confirmed the validity of the proposed method.

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On-line overlaid-handwriting recognition based on substroke HMMs

Cited by 15 publications

References 6 publications

Text Input System Using Online Overlapped Handwriting Recognition for Mobile Devices

Text Input System Using Online Overlapped Handwriting Recognition for Mobile Devices

Character-Position-Free On-Line Handwritten Japanese Text Recognition by Two Segmentation Methods

Overlapped Handwriting Input on Mobile Phones

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