Fundamentals of speech recognition
Fundamentals of speech recognition
Digital Image Processing
A New Method for Segmenting Unconstrained Handwritten Numeral String
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A System for Segmentation and Recognition of Totally Unconstrained Handwritten Numeral Strings
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Algorithms for Partitioning Path Construction of Handwritten Numeral Strings
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Integrated segmentation and recognition of handwritten numeralswith cascade neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Touching numeral segmentation using water reservoir concept
Pattern Recognition Letters
Recognition of Cursive Roman Handwriting - Past, Present and Future
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
An approach for locating segmentation points of handwritten digit strings using a neural network
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
A Novel Approach to Separate Handwritten Connected Digits
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Segmentation of Bangla Unconstrained Handwritten Text
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A Synthetic Database to Assess Segmentation Algorithms
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Segmentation of Connected Chinese Characters Based on Genetic Algorithm
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Segmentation of Connected Handwritten Numerals by Graph Representation
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Filtering segmentation cuts for digit string recognition
Pattern Recognition
Character segmentation and recognition algorithm of text region in steel images
ISPRA'09 Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation
A metasynthetic approach for segmenting handwritten Chinese character strings
Pattern Recognition Letters
Multi-oriented Bangla and Devnagari text recognition
Pattern Recognition
Electronic reading pen: a DSP based portable device for offline OCR and bi-linguistic translation
ICESS'04 Proceedings of the First international conference on Embedded Software and Systems
Assessing handwitten digit segmentation algorithms
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Segmentation of connected handwritten digits using Self-Organizing Maps
Expert Systems with Applications: An International Journal
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A new approach of segmenting single- or multiple-touching handwritten numeral string (two-digits) is proposed. Most algorithms for segmenting connected digits mainly focus on the analysis of foreground pixels. Some concentrated on the analysis of background pixels only and others are based on a recognizer. In this paper, we combine background and foreground analysis to segment single- or multiple-touching handwritten numeral strings. Thinning of both foreground and background regions are first processed on the image of connected numeral strings and the feature points on foreground and background skeletons are extracted. Several possible segmentation paths are then constructed and useless strokes are removed. Finally, the parameters of geometric properties of each possible segmentation paths are determined and these parameters are analyzed by the mixture Gaussian probability function to decide the best segmentation path or reject it. Experimental results on NIST special database 19 (an update of NIST special database 3) and some other images collected by ourselves show that our algorithm can get a correct rate of 96 percent with rejection rate of 7.8 percent, which compares favorably with those reported in the literature.