Handwritten Signatures Recognizer by Its Envelope and Strokes Layout Using HMM's

JA Sanchez,CM Travieso,IG Alonso,MA Ferrer

Cited 17|Views2

Abstract

A method for the automatic recognition of offline handwritten signatures using both global and local features is described. As global features, we use the envelope of the signature sequenced as polar coordinates; and as local features we use points located inside the envelope that describe the density or distribution of signature strokes. Each feature is processed as a sequence by a hidden Markov Model (HMM) classifier. The results of both classifiers are linearly combined, obtaining a recognition ratio of 95.15% with a database of 60 handwritten signatures

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Key words

handwriting recognition,handwritten character recognition,hidden Markov models,image recognition,HMM,automatic recognition,global features,handwritten signature database,handwritten signature recognizer,hidden Markov model classifier,local features,offline handwritten signature recognition,polar coordinates,recognition ratio,signature strokes,stroke layout

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【要点】：本文提出了一种结合全局特征（签名外轮廓）和局部特征（笔迹分布）的手写签名自动识别方法，使用隐马尔可夫模型（HMM）进行分类，实现了95.15%的识别率。

【方法】：通过将签名外轮廓转换为极坐标序列作为全局特征，并将签名内部点描述的笔迹密度或分布作为局部特征，使用HMM分类器处理每种特征序列。

【实验】：在包含60个手写签名的数据库上进行实验，通过线性组合全局和局部特征分类器的结果，达到了95.15%的识别率。

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