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Abstract

Writer identification consists of identifying the writer of a certain handwritten document and is of high importance in forensic document examination. Indeed, numerous cases over the years have dealt with evidence provided by handwritten documents such as wills and ransom notes. Automatic methods for writer identification can be divided into codebook-based and feature-based approaches. In codebook-based approaches, the writer is assumed to act as a stochastic generator of graphemes. The probability distribution of grapheme usage is used to distinguish between writers. Feature-based approaches compare the handwritings according to some geometrical, structural or textural features. Feature-based approaches prove to be efficient and are generally preferred when there is a limited amount of available handwriting. Therefore, we are more interested in this study in this category of approaches. Writer identification is performed by comparing a query document to a set of known documents and assigning it to the closest document in term of similarity of handwriting. This is done by extracting characterizing features from the documents including: directions, curvatures, tortuosities (or smoothness), chain codes distributions and edge based directional features. These features correspond to probability distribution functions (PDF) extracted from the handwriting images to characterize writer individuality. When matching a query document against any other document, the ¬χ2 distance between their corresponding features is computed. The smaller the distance, the more likely the two documents are written by the same writer. Therefore, the identified writer is the one of the document with the smallest distance to the query document. The writer is said to be correctly identified when the identified writer corresponds to the actual writer of the query document. The performance has been evaluated on the IAM handwriting dataset, with chain code based features generally outperforming the other features reaching 71% correct identification rate. The combination of all the features lead to 76% correct identification rate. The proposed system also won the music scores writer identification contest reaching 77% identification rate. The proposed method automatically extracts features used by forensic experts in order to identify writers of handwritten document. The results show that the method is efficient and language-independent.

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/content/papers/10.5339/qfarf.2012.CSP29
2012-10-01
2020-01-27
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarf.2012.CSP29
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