Segmentation and Recognition of Handwritten Numeric Chains | Science Publications

Journal of Computer Science

Mouldi Bedda¹  Abderrazak Lachouri¹  Salim Ouchtati¹ 

关键词: Optical Characters Recognition;    Neural networks;    Barr features;    Image processing;    Pattern Recognition;    Features extraction;   

DOI  :  10.3844/jcssp.2007.242.248

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

Automatic reading of numeric chains has been attempted in several application areas such as bank cheque processing, postal code recognition and form processing. Such applications have been very popular in handwriting recognition research, due to the possibility to reduce considerably the manual effort involved in these tasks. In this study we propose an off line system for the recognition of the handwritten numeric chains. Firstly, study was based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. Used parameters to form the input vector of the neural network are extracted on the binary images of the digits by several methods: distribution sequence, Barr features and centred moments of different projections and profiles. Secondly, study was extented for the reading of the handwritten numeric chains constituted of a variable number of digits. Vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) waspresented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits). The performances of the proposed system for the used database attain a recognition rate equal to 91.3%.

【 授权许可】

Unknown