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Over-segmentation and validation strategy for off-line cursive handwriting recognition

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Over-segmentation and validation strategy for off-line cursive handwriting recognition
นักวิจัย : Lee, Hong. , Verma, Brijesh.
คำค้น : Applied research. , 890202 Application Tools and System Utilities. , 080109 Pattern Recognition and Data Mining. , 080108 Neural, Evolutionary and Fuzzy Computation. , Pattern recognition systems. , Neural networks (Computer science) , Writing , Off-line handwriting recognition -- Segmentation -- Neural networks
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2551
อ้างอิง : http://hdl.cqu.edu.au/10018/28544 , http://dx.doi.org/10.1109/ISSNIP.2008.4761968. , cqu:4371
ที่มา : Lee, H & Verma, B 2008, 'Over-segmentation and validation strategy for off-line cursive handwriting recognition', International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Sydney, 15-18 December 2008, IEEE, pp. 91-96. http://dx.doi.org/10.1109/ISSNIP.2008.4761968
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2008), 15-18 December 2008, Sydney, Australia. USA. : IEEE, 2008. p. 91-96 6 pages Refereed 9781424429578 (online) , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

This paper presents an over-segmentation and validation strategy for off-line cursive handwriting recognition. Over-segmentation module is employed to find all the possible character boundaries. Then, the incorrect segmentation points from over-segmenting module are removed by validating processes. The over-segmentation was performed based on the vertical pixel density between upper and lower baselines. Wherever the pixel density is less than threshold, an over-segmentation point is assigned. After the over-segmentation is done, validation starts removing over-segmentation points. The first validation module checks if a segmentation point lies in hole region. The second validation module compares total foreground pixel between two neighbouring segmentation points to a threshold value. The third validation module is neural network voting by neural network classifier trained on pre-segmented characters. Finally, the oversized segment validation process checks if there is any missing segmentation point between neighbouring characters. The proposed approach has been implemented, and the experiments on CEDAR benchmark database have been conducted. The results of the experiments are very promising and the overall performance of the algorithm is more effective than the other existing segmentation algorithms.

บรรณานุกรม :
Lee, Hong. , Verma, Brijesh. . (2551). Over-segmentation and validation strategy for off-line cursive handwriting recognition.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Lee, Hong. , Verma, Brijesh. . 2551. "Over-segmentation and validation strategy for off-line cursive handwriting recognition".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Lee, Hong. , Verma, Brijesh. . "Over-segmentation and validation strategy for off-line cursive handwriting recognition."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2551. Print.
Lee, Hong. , Verma, Brijesh. . Over-segmentation and validation strategy for off-line cursive handwriting recognition. กรุงเทพมหานคร : Central Queensland University, Australia; 2551.