On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Recognition of off-line handwritten devnagari characters using quadratic classifier
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
An overview of character recognition focused on off-line handwriting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Integrating knowledge sources in Devanagari text recognition system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A statistical-topological feature combination for recognition of handwritten numerals
Applied Soft Computing
The study of different similarity measure techniques in recognition of handwritten characters
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Recognition of alphabetic characters is a basic need in incorporating intelligence to computers. Machine intelligence involves several aspects among which optical recognition is a tool, which can be integrated to text recognition. To make these aspects effective character recognition with better accuracy is important. However, handwritten character recognition is still a difficult task because of the high variability in the character shapes written by individuals. While large amount of work has been done towards recognition of handwritten English characters relatively less work is reported for the recognition of Indian language scripts. So, we proposed a new elastic image matching (EM) technique based on an eigen-deformation for recognition of offline isolated English uppercase handwritten characters and offline isolated handwritten characters of Devnagari, the most popular script in India. Deformations in handwritten characters have category-dependent tendencies. The estimation and the utilization of such tendencies called eigen-deformations are investigated for the better performance of elastic matching based handwritten character recognition. The eigen-deformations are estimated by the principal component analysis of actual deformations automatically collected by the elastic matching. Typical deformations of each category can be extracted as the eigen-deformations. According to a similarity measure (e.g.: Euclidean, Mahalanobis similarity measures etc.), a prototype matching is done for recognition.