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An end-to-end architecture for multi-script document retrieval usinghandwritten signatures is proposed in this paper. The user supplies a querysignature sample and the system exclusively returns a set of documents thatcontain the query signature. In the first stage, a component-wiseclassification technique separates the potential signature components from allother components. A bag-of-visual-words powered by SIFT descriptors in apatch-based framework is proposed to compute the features and a Support VectorMachine (SVM)-based classifier was used to separate signatures from thedocuments. In the second stage, features from the foreground (i.e. signaturestrokes) and the background spatial information (i.e. background loops,reservoirs etc.) were combined to characterize the signature object to matchwith the query signature. Finally, three distance measures were used to match aquery signature with the signature present in target documents for retrieval.The `Tobacco' document database and an Indian script database containing 560documents of Devanagari (Hindi) and Bangla scripts were used for theperformance evaluation. The proposed system was also tested on noisy documentsand promising results were obtained. A comparative study shows that theproposed method outperforms the state-of-the-art approaches.

Bag-of-Visual-Words for Signature-Based Multi-Script Document Retrieval