During the last two decade, numerous handwriting character recognition systems have been proposed. Many of them presented their limitation when the handwriting character is cursive type and it has some deformation. However this type of cursive character is easily recognized by the human being. In this paper we research its human ability and apply it to the dynamic handwritting character recognition. In the proposed system, significant knots of each character are extracted using natural Spline function named SLALOM and their position is optimized Steepest Descent Method. Using a training set consisting of the sequence of optimal knots, each character model will be constructed. Finally the unknown input character will be compared with each model of all characters to get the similarity scores. The character model with higher similarity score will be considered as the recognized character of the input data. The recognition stage consists in two-steps: classification using global feature and classification using local feature.
Cursive Character Recognition System
2006-09-01
185310 byte
Conference paper
Electronic Resource
English
On-Line Cursive Korean Character Recognition by Using Curvature Models
British Library Conference Proceedings | 1995
|On-Line Cursive Kanji Character Recognition as Stroke Correspondence Problem
British Library Conference Proceedings | 1995
|Persian Cursive Script Recognition
British Library Conference Proceedings | 1995
|Cursive Arabic Script Segmentation and Recognition System
British Library Online Contents | 2005
|Recognition of Off-Line Cursive Handwriting
British Library Online Contents | 1998
|