Results of IPTP Character Recognition Competitions and Studies on Multi-expert System for Handprinted Numeral Recognition (Special Issue on Character Recognition and Document Understanding)

概要

論文の詳細を見る
Through comparing the results of two successive IPTP Character Recognition Competitions which focused on 3-digit handprinted postal codes, we herein analyze the methodologies of the submitted algorithms along with the substituted or rejected patterns of these algorithms. Regarding their methodologies, lesser diversity was apparent specifically concerning the contour-chain code based on local stroke directions and statistical discriminant functions for feature extraction and discrimination. Analysis of the patterns demonstrated that the misrecognized patterns being most often improved were categorized as a decrease in peculiarly shaped handwritten characters or heavy-handed and disconnected strokes. However, most of the remaining misrecognitions were still classed as peculiarly shaped handwriting as commonly shared between the best three algorithms. From these analyses, we could delineate a direction to be taken for developing more effective methodologies and clarify the remaining problems to be overcome by the subsequent intensive research. Furthermore, we evaluate in this article our multi-expert recognition system for achieving higher recognition performances by means of combining complementary recognition algorithms. We performed a subsequent investigation of the Candidate Appearance Likelihood Method using novel experimental conditions and a new examination of the application of the neural network as the combining method for accumulating the broader candidate appearances. The results obtained confirm that combining through the neural network constitutes one of the most effective ways of making the multi-expert recognition system a reality.
社団法人電子情報通信学会の論文
1996-05-25

Results of IPTP Character Recognition Competitions and Studies on Multi-expert System for Handprinted Numeral Recognition (Special Issue on Character Recognition and Document Understanding)

スポンサーリンク

概要

著者

関連論文

スポンサーリンク