Large-scale signature matching using multi-stage hashing

In this paper, we propose a fast large-scale signature matching method based on locality sensitive hashing (LSH). Shape Context features are used to describe the structure of signatures. Two stages of hashing are performed to find the nearest neighbours for query signatures. In the first stage, we use M randomly generated hyper planes to separate shape context feature points into different bins, and compute a term-frequency histogram to represent the feature point distribution as a feature vector. In the second stage we again use LSH to categorize the high-level features into different classes. The experiments are carried out on two datasets - DS-I, a small dataset contains 189 signatures, and DS-II, a large dataset created by our group which contains 26,000 signatures. We show that our algorithm can achieve a high accuracy even when few signatures are collected from one same person and perform fast matching when dealing with a large dataset.