On-line cursive Kanji character recognition as stroke correspondence problem

Wakahara, Toru; Suzuki, Akira; Nakajima, Naoki; Miyahara, Shiho; Odaka, Kento

doi:10.1109/icdar.1995.602091

Cited by 16 publications

(19 citation statements)

References 3 publications

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“…However, these methods raise the recognition rate by using stroke order information. Techniques incorporating variations of stroke order and stroke number have been reported as in Wakahara and Suzuki [1995], and some commercial products are incorporating them. Still, when the strokes are written randomly, the recognition usually decreases.…”

Section: Conventional Methods and Related Workmentioning

confidence: 98%

Kansuke

Tanaka-Ishii

Godon

2009

ACM Trans. Comput.-Hum. Interact.

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We have developed a method that makes it easier for language novices to look up Japanese and Chinese logographs. Instead of using the arbitrary conventions of logographs, this method is based on three simple prototypes: horizontal, vertical, and other strokes. For example, the code for the logograph (ta, meaning rice field) is 3-3-0, indicating the logograph consists of three horizontal strokes and three vertical strokes. Such codes allow a novice to look up logographs even with no knowledge of the logographic conventions used by native speakers. To make the search easier, a complex logograph can be looked up via the components making up the logograph. We conducted a user evaluation of this system and found that novices could look up logographs with fewer failures with our system than with conventional methods. ACM Reference Format:Tanaka-Ishii, K. and Godon, J. 2009. Kansuke: A logograph look-up interface based on a few modified stroke prototypes. ACM Trans.

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Section: Conventional Methods and Related Workmentioning

confidence: 98%

Kansuke

Tanaka-Ishii

Godon

2009

ACM Trans. Comput.-Hum. Interact.

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“…The optimal correspondence which satisfies the above five conditions is achieved by efficient combinatorial search [14]. The threshold Th d is set as approximately 12 pixels, which is the accuracy of fine shift matching, and the threshold Th θ is set as approximately 30°.…”

Section: Correspondence Of Minutiaementioning

confidence: 99%

Fingerprint verification using ridge direction distribution and minutiae correspondence

Wakahara

Kimura

Suzuki

et al. 2007

Systems & Computers in Japan

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SUMMARYFingerprint verification technique is raising high expectations as a means of personal identification based on physical features. This paper proposes a fingerprint verification method which can achieve minutiae matching with highly accurate compensation for rotation and position deviation, as well as high speed and stability. The process flow after binarization of the gray-scale fingerprint image is as follows. (1) Using the fingerprint ridge direction distribution in each local region, highly accurate compensation for the rotation and the position deviation between the enrolled fingerprint and the input fingerprint is performed. (2) The optimal correspondence between enrolled and input minutiae is established by fast combinatorial search. (3) By applying threshold processing to the distances of the fingerprint ridge direction distributions and the minutiae matching rate, the acceptance/rejection of the person is decided. Fingerprint image data for 80 persons × 4 fingers × 10 samples were collected using a commercial electrostatic capacitance semiconductor fingerprint sensor. The false reject rate and the false acceptance rate are investigated in fingerprint verification experiments, and the effectiveness of the proposed method is demonstrated.

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“…Some stroke-number and stroke-order free recognition methods have been proposed [8,10]. Also, we have proved that substroke HMM based method does not depend on the stroke-number [7] since the pen-up-down information is not utilized for handwriting features.…”

Section: Introductionmentioning

confidence: 99%

“…Also, we have proved that substroke HMM based method does not depend on the stroke-number [7] since the pen-up-down information is not utilized for handwriting features. On the stroke-order problem, there are mainly two approaches; first one is to register different reference patterns in the dictionary [1,4] and second one is to search the stroke-order simultaneously in a decoding process [8,10]. In the stroke-based model, the second approach is useful and all possible stroke-order variations can be expressed as a permutation of stroke models.…”

Section: Introductionmentioning

confidence: 99%

Generation of hierarchical dictionary for stroke-order free Kanji handwriting recognition based on substroke HMM

Nakai

Shimodaira

Sagayama

Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.

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This paper describes a method of generating a Kanji hierarchical structured dictionary for stroke-number and stroke-order free handwriting recognition based on substroke HMM. In stroke-based methods, a large number of stroke-order variations can be easily expressed by just adding different stroke sequences to the dictionary and it is not necessary to train new reference patterns. The hierarchical structured dictionary has an advantage that thousands of stroke-order variations of Kanji characters can be produced using a small number of stroke-order rules defining Kanji parts. Moreover, the recognition speed is fast since common sequences are shared in a substroke network, even if the total number of stroke-order combinations becomes enormous practically. In experiments, 300 different stroke-order rules of Kanji parts were statistically chosen by using 60 writers' handwritings of 1,016 educational Kanji characters. By adding these new stroke-order rules to the dictionary, about 9,000 variations of different stroke-orders were generated for 2,965 JIS 1st level Kanji characters. As a result, we successfully improved the recognition accuracy from 82.6% to 90.2% for stroke-order free handwritings.

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On-line cursive Kanji character recognition as stroke correspondence problem

Cited by 16 publications

References 3 publications

Kansuke

Kansuke

Fingerprint verification using ridge direction distribution and minutiae correspondence

Generation of hierarchical dictionary for stroke-order free Kanji handwriting recognition based on substroke HMM

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