28 March 1995 Handwriting recognition using a reduced character method and neural nets
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Abstract
This paper deals with the recognition of handwriting text character by using a reduced character methodology and neural nets (BNN, RNN). The reduced characters methodology is based on the representation (mapping) of the text characters on a small size 2-D array of 12 X 9. For the recognition process each character is considered as a composition of `main' and `secondary' features. The main features are the necessary and important parts of a character for its recognition. The secondary (or artistic) features are the parts of a character which contribute to its various representations. The reduced character methodology presented in this paper attempts to prove that the recognition of a reduced size character provides a robust approach for recognition of handwritten text. The RNN approach for handwritten character recognition is based upon recurrent neural networks. The recurrent networks have a feedback mechanism. The feedback mechanism acts to integrate new values of feature vector with their predecessors. The output is supervised according to a target function. These networks can deal with inputs and outputs that are explicit functions of time. A new way of associating shape information was used, which gives very consistent results for handwritten character recognition. In this scheme the `shadow' each character was considered to find was the distances between the margins of the character. The distances are normalized with respect to the maximum distance in the entire shape to minimize the effect of disproportionately formed characters. For this effort two neural networks and an attributed graph approach are used and their results are compared on a set of 5000 handwritten characters.
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Nikolaos G. Bourbakis, Cris Koutsougeras, A. Jameel, "Handwriting recognition using a reduced character method and neural nets", Proc. SPIE 2424, Nonlinear Image Processing VI, (28 March 1995); doi: 10.1117/12.205260; https://doi.org/10.1117/12.205260
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