A Deep Learning Approach for Arabic Manuscripts Classification

Abstract

For centuries, libraries worldwide have preserved ancient manuscripts due to their immense historical and cultural value. However, over time, both natural and human-made factors have led to the degradation of many ancient Arabic manuscripts, causing the loss of significant information, such as authorship, titles, or subjects, rendering them as unknown manuscripts. Although catalog cards attached to these manuscripts might contain some of the missing details, these cards have degraded significantly in quality over the decades within libraries. This paper presents a framework for identifying these unknown ancient Arabic manuscripts by processing the catalog cards associated with them. Given the challenges posed by the degradation of these cards, simple optical character recognition (OCR) is often insufficient. The proposed framework uses deep learning architecture to identify unknown manuscripts within a collection of ancient Arabic documents. This involves locating, extracting, and classifying the text from these catalog cards, along with implementing processes for region-of-interest identification, rotation correction, feature extraction, and classification. The results demonstrate the effectiveness of the proposed method, achieving an accuracy rate of 92.5%, compared to 83.5% with classical image classification and 81.5% with OCR alone.

The Urgency of Digitizing Ancient Indonesian Manuscripts in Libraries: A Literature Review

“…Further exploration of the urgency of digitizing Indonesian ancient manuscripts in the context of libraries is essential to investigate (Al-homed et al, 2023). It is important to understand the benefits of digitization in addressing various issues related to these manuscripts (Makhfi et al, 2011) and to discuss the role of information technology in preserving and utilizing this priceless cultural heritage (Cissé, 2020;Fermanian et al, 2022).…”