Bag-of-Visual-Words for Signature-Based Multi-Script Document Retrieval
Ranju Mandal, Partha Pratim Roy, Umapada Pal, Michael, Blumenstein

TL;DR
This paper introduces an end-to-end system for multi-script document retrieval based on handwritten signatures, utilizing a bag-of-visual-words approach with SIFT descriptors and SVM classification to accurately match signatures in diverse documents.
Contribution
The paper presents a novel multi-stage architecture combining component classification, bag-of-visual-words features, and spatial signature characterization for signature-based document retrieval.
Findings
Outperforms state-of-the-art methods on tobacco and Indian script databases.
Effective in noisy document conditions.
Achieves promising retrieval accuracy across multiple scripts.
Abstract
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…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Image Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques
