Fourier Preprocessing for Hand Print Character Recognition
IEEE Transactions on Computers
Recognition of Handprinted Bangla Numerals Using Neural Network Models
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
A Majority Voting Scheme for Multiresolution Recognition of Handprinted Numerals
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Oriya Handwritten Numeral Recognition Syste
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A handwritten Bangla numeral recognition scheme based on expanded two-layer SOM
International Journal of Intelligent Systems Technologies and Applications
Topological features for recognizing printed and handwritten Bangla characters
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
A MLP classifier for both printed and handwritten bangla numeral recognition
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
On recognition of handwritten bangla characters
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Detection of structural concavities in character images--a writer-independent approach
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Recognition of Bangla compound characters using structural decomposition
Pattern Recognition
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This paper deals with an automatic recognition method for unconstrained off-line Bangla handwritten numerals. To take care of variability involved in the writing style of different individuals a robust scheme is presented here. The scheme is based on new features obtained from the concept of water overflow from the reservoir as well as topological and statistical features of the numerals. If we pour water from upper part of the character, the region where water will be stored in the character is imagined as a reservoir of the character. The direction of water overflow, height of water level when water overflows from the reservoir, position of the reservoir with respect to the character bounding box, shape of the reservoir etc. are used in the recognition scheme. The proposed scheme is tested on data collected from different individuals of various background and we obtained an overall recognition accuracy of about 91.98%.