Arabic Manuscript Content Based Image Retrieval: A Comparison between SURF and BRISK Local Features

Abstract

Arabic manuscripts are worthy sources of knowledge that have been highly underutilized. Because, the vast content of the Arabic manuscript and the need of getting information from them, in a fast, efficient, and accurate way, it is essential to develop a system that supports the retrieval procedure from them. In this paper, a Content-Based Image Retrieval (CBIR) system is proposed to retrieve the Arabic manuscript images. The system has three stages: Preprocessing, feature extraction, and feature similarity matching. The features extraction techniques are the effective step for the performance of CBIR system. For this reason, we propose to apply Binary Robust Invariant Scalable Key points (BRISK) and Speeded-up Robust Feature (SURF) as features extraction techniques. The Hamming distance with BRISK and Sum of square differences (SSD) with SURF are used at the matching stage. The results of proposed system show that for SURF the average Recall is 85% and average Precision is 77%. The average time is 207.3 seconds per image. For BRISK, the average Recall is 69% and average Precision is 68%. The average time is 256.7 seconds per image. The SURF features yield the best performance for Arabic manuscript retrieval. For better time performance of the system we propose to use parallel computing as a future work.