A Word Spotting Method for Arabic Manuscripts Based on Speeded Up Robust Features Technique

Abstract

The diversity of manuscripts according to their contents, forms, organizations and presentations provides a data-rich structures. The aim is to disseminate this cultural heritage in the images format to the general public via digital libraries. However, handwriting is an obstacle to text recognition algorithms in images, especially cursive writing of Arabic calligraphy. Most current search engines used by digital libraries are based on metadata and structured data manually transcribed in Ascii format. In this article, we propose an original method of pattern recognition for searching the content of Arabic handwritten documents based on the Word Spotting technique. Our method is both effective and simple, it consists in extracting a set of features from the words we segment in the target images and comparing them with the features of the words in the requested images. The principle of the method is to characterize each word with the Speeded Up Robust Features algorithm whose goal is to find all occurrences of query words in the target image even in the case of low-resolution images. We tested our method on hundreds of pages of Arabic manuscripts from the National Library of Rabat and the Digital Library of Leipzig University. The results obtained are encouraging compared to other methods based on the same Word Spotting technique.

Manuscripts Image Retrieval Using Deep Learning Incorporating a Variety of Fusion Levels

“…El Makhfi [42] proposed recognizing the written words within the Arabic manuscripts' images through extracting the handcrafted features. The authors started by preprocessing the input query image through converting it into grey-scale version then, segmenting it into lines and words.…”

“…From table 14, we notice that seven papers [8], [19], [33], [34], [35] [40], and [42] addressed the retrieval of the Arabic manuscript' images. However, all of them used the handcrafted features.…”