Bag-of-Visual-Words for Signature-Based Multi-Script Document Retrieval
This addresses document retrieval for users handling multi-script archives, but it is incremental as it builds on existing bag-of-visual-words and SIFT methods.
The paper tackles the problem of retrieving multi-script documents based on handwritten signatures by proposing an end-to-end architecture that uses bag-of-visual-words with SIFT descriptors and SVM classification, achieving results that outperform state-of-the-art approaches on databases like Tobacco and an Indian script set.
An end-to-end architecture for multi-script document retrieval using handwritten signatures is proposed in this paper. The user supplies a query signature sample and the system exclusively returns a set of documents that contain the query signature. In the first stage, a component-wise classification technique separates the potential signature components from all other components. A bag-of-visual-words powered by SIFT descriptors in a patch-based framework is proposed to compute the features and a Support Vector Machine (SVM)-based classifier was used to separate signatures from the documents. In the second stage, features from the foreground (i.e. signature strokes) and the background spatial information (i.e. background loops, reservoirs etc.) were combined to characterize the signature object to match with the query signature. Finally, three distance measures were used to match a query signature with the signature present in target documents for retrieval. The `Tobacco' document database and an Indian script database containing 560 documents of Devanagari (Hindi) and Bangla scripts were used for the performance evaluation. The proposed system was also tested on noisy documents and promising results were obtained. A comparative study shows that the proposed method outperforms the state-of-the-art approaches.