Online handwriting recognition using multi convolution neural networks

Authors:
Dũng Viṿt Phạm
Affiliations:
Computer Network Centre, Vietnam Maritime University, Hai Phong city, Vietnam
Venue:
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Year:
2012

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Abstract

This paper presents a library written by C# language for the online handwriting recognition system using UNIPEN-online handwritten training set. The recognition engine based on convolution neural networks and yields recognition rates to 99% to MNIST training set, 97% to UNIPEN's digit training set (1a), 89% to a collection of 44022 capital letters and digits (1a,1b) and 89% to lower case letters (1c). These networks are combined to create a larger system which can recognize 62 English characters and digits. A proposed handwriting segmentation algorithm is carried out which can extract sentences, words and characters from handwritten text. The characters then are given as the input to the network.