Character recognition without segmentation

Character recognition without segmentation (1995)

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by Jairo Rocha , The Pavlidis

Venue:	IEEE transaction on Pattern Analysis and Machine Intelligence, 17(9):903 -- 909
Citations:	9 - 0 self

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Datum	Value	Source
TITLE	Character recognition without segmentation	INFERENCE
AUTHOR NAME	Jairo Rocha	SVM HeaderParse 0.2
AUTHOR NAME	The Pavlidis	SVM HeaderParse 0.2
ABSTRACT	Abstract-A segmentation-free approach to OCR is presented as part of a knowledge-based word interpretation model. This new method is based on the recognition of subgraphs homeomorphic to previously de-fined prototypes of characters [16]. Gaps are identified as potential parts of characters by implementing a variant of the notion of relative neigh-borhood used in computational perception. In the system, each subgraph of strokes that matches a previously defined character prototype is rec-ognized anywhere in the word even if it corresponds to a broken charac-ter or to a character touching another one. The characters are detected in the order defined by the matching quality. Each subgraph that is rec-ognized is introduced as a node in a directed net that compiles different alternatives of interpretation of the features in the feature graph. A path in the net represents a consistent succession of characters in the word. The method allows the recognition of characters that overlap or that are underlined. A final search for the optimal path under certain criteria gives the best interpretation of the word features. The character recog-nizer uses a flexible matching between the features and a flexible gronp-ing of the individual features to be matched. Broken characters are rec-ognized by looking for gaps between features that may be interpreted as part of a character. Touching characters are recognized because the matching allows nonmatched adjacent strokes. The recognition results of this system for over 24,OOO printed numeral characters belonging to a USPS database and on some hand-printed words confirmed the method’s high robustness level. Index Tenns-Character recognition without segmentation, broken character recognition, touching character recognition, homeomorphic subgraph matching, relative neighborhood graph. I.	SVM HeaderParse 0.2
YEAR	1995	INFERENCE
VENUE	IEEE transaction on Pattern Analysis and Machine Intelligence, 17(9):903 -- 909	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	903--909	INFERENCE
VOLUME	17	INFERENCE
CITATIONS	19 found	ParsCit 1.0