Multi-channel handwritten digit recognition using neural networks

Zheru Chi, Zhongkang Lu, Fai hung Chan

Research output: Journal article publicationConference articleAcademic researchpeer-review

2 Citations (Scopus)


Human recognition is much more robust than machine recognition in dealing with rotated and noisy patterns. In this paper, we present a multi-channel neural network (MCNN) approach for handwritten digit recognition based on the human recognition experience in the hope of achieving human-like performance. In this approach, three neural network modules are trained individually by using three different set of features, intensity-based, rotation invariant, and noise deducted features. The outputs of these three modules are then combined by a combination neural network which is trained separately. Experimental results on a database of 1900 digit patterns written by 190 people show that a recognition rate of 89.5% is obtained on an independent test set that includes both the rotated and noisy data.
Original languageEnglish
Pages (from-to)625-628
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
Publication statusPublished - 1 Jan 1997
EventProceedings of the 1997 IEEE International Symposium on Circuits and Systems, ISCAS'97. Part 4 (of 4) - Hong Kong, Hong Kong
Duration: 9 Jun 199712 Jun 1997

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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