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Download fileA contour code feature based segmentation for handwriting recognition
conference contribution
posted on 2017-12-06, 00:00 authored by Brijesh VermaThe purpose of this paper is to present a novel contour code feature in conjunction with a rule based segmentation for cursive handwriting recognition. A heuristic segmentation algorithm is initially used to over segment each word. Then the prospective segmentation points are passed through the rule-based module to discard the incorrect segmentation points and include any missing segmentation points. The proposed rule-based module validates every segmentation points against closed area, average character size, left character and density. During the left char validation, a contour code feature is extracted and checked weather the left of the prospective segmentation point is a character or rubbish (non-char). The neural network used for this validation was trained on character and non-character database. Following the segmentation, the contour between correct segmentation points is passed through the feature extraction module that extracts the contour code, after which another trained neural network is used for classification. The recognized characters are grouped into words and passed to a variable length lexicon that retrieves words that has highest confidence value.
History
Parent Title
Proceedings of Seventh International Conference on Document analysis and recognition (ICDAR'03), United Kingdom, 3-6 August, 2003.Start Page
1203End Page
1207Number of Pages
5Start Date
2003-01-01Finish Date
2003-01-01ISBN-10
0769519601Location
Edinburgh, ScotlandPublisher
IEEE Computer SocietyPlace of Publication
United StatesAdditional Rights
CC-BY-NC-NDPeer Reviewed
- Yes
Open Access
- Yes
Era Eligible
- Yes