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

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 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.