Recognition of Unconstrained Handwritten Numeral Strings Using Decision Value Generator

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ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Year:
2001

Citing 0
Cited 2

Effects of Classifier Structures and Training Regimes on Integrated Segmentation and Recognition of Handwritten Numeral Strings

IEEE Transactions on Pattern Analysis and Machine Intelligence
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MMACTE'05 Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical Engineering

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Abstract

Abstract: This paper presents recognition of unconstrained handwritten numeral strings using decision value generator. The numeral string recognition system is composed of three modules, pre-segmentation, segmentation and recognition. The pre-segmentation module classifies a numeral string into sub-images, such as isolated digits, touching digits or broken digits, based on the confidence value of decision value generator. The segmentation module splits the touching digits using the reliability value of decision value generator. Both segmentation-based and segmentation-free methods are used in classification and segmentation. To evaluate the proposed method, experiments have been conducted using the handwritten numeral strings of NIST SD19 and higher recognition performance than previous works has been obtained.