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

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conference contribution
posted on 06.12.2017, 00:00 by Hong Suk Lee, Brijesh Verma
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.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Page

91

End Page

96

Number of Pages

6

Start Date

01/01/2008

ISBN-13

9781424429578

Location

Sydney, Australia

Publisher

IEEE

Place of Publication

USA

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Business and Informatics;

Era Eligible

Yes

Name of Conference

Intelligent Sensors, Sensor Networks & Information Processing Conference.