NDLI: A novel multiple experts and fusion based segmentation algorithm for cursive handwriting recognition

Content Provider	IEEE Xplore Digital Library
Author	Hong Lee Verma, B.
Copyright Year	2008
Description	Author affiliation: Central Queensland Univ., Rockhampton, QLD (Hong Lee; Verma, B.)
Abstract	This 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.
Starting Page	2994
Ending Page	2999
File Size	275526
Page Count	6
File Format	PDF
ISBN	9781424418206
ISSN	10987576
DOI	10.1109/IJCNN.2008.4634219
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Classification algorithms Handwriting recognition Accuracy Algorithm design and analysis Pixel Pattern recognition
Content Type	Text
Resource Type	Article

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