A novel multiple experts and fusion based segmentation algorithm for cursive handwriting recognition
conference contribution
posted on 2017-12-06, 00:00 authored by Hong Suk LeeHong Suk Lee, Brijesh VermaThis paper presents a novel segmentation algorithm for offline cursive handwriting recognition. An over-segmentation algorithm is introduced to dissect the words from handwritten text based on the pixel density between upper and lower baselines. Each segment from the over-segmentation is passed to a multiple expert-based validation process. First expert compares the total foreground pixel of the segmentation point to a threshold value. The threshold is set and calculated before the segmentation by scanning the stroke components in the word. Second expert checks for closed areas such as holes. Third expert validates segmentation points using a neural voting approach which is trained on segmented characters before validation process starts. Final expert is based on oversized segment analysis to detect possible missed segmentation points. The proposed algorithm has been implemented and the experiments on cursive handwritten text 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
2994End Page
2999Number of Pages
6Start Date
2008-01-01ISBN-13
9781424418213Location
Hong KongPublisher
IEEEPlace of Publication
USAPublisher DOI
Full Text URL
Peer Reviewed
- Yes
Open Access
- No
External Author Affiliations
Faculty of Business and Informatics; International Joint Conference on Neural Networks (2008 : Hong Kong) [electronic resource];Era Eligible
- Yes