Building a multi-modal Arabic corpus (MMAC)

Abstract

Traditionally, a corpus is a large structured set of text, electronically stored and processed. Corpora have become very important in the study of languages. They have opened new areas of linguistic research, which were unknown until recently. Corpora are also key to the development of optical character recognition (OCR) applications. Access to a corpus of both language and images is essential during OCR development, particularly while training and testing a recognition application. Excellent corpora have been developed for Latin-based languages, but few relate to the Arabic language. This limits the penetration of both corpus linguistics and OCR in Arabic-speaking countries. This paper describes the construction and provides a comprehensive study and analysis of a multi-modal Arabic corpus (MMAC) that is suitable for use in both OCR development and linguistics. MMAC currently contains six million Arabic words and, unlike previous corpora, also includes connected segments or pieces of Arabic words (PAWs) as well as naked pieces of Arabic words (NPAWs) and naked words (NWords); PAWs and Words without diacritical marks. Multi-modal data is generated from both text, gathered from a wide variety of sources, and images of existing documents. Text-based data is complemented by a set of artificially generated images showing each of the Words, NWords, PAWs and NPAWs involved. Applications are provided to generate a natural-looking degradation to the generated images. A ground truth annotation is offered for each such image, while natural images showing small paragraphs and full pages are augmented with representations of the text they depict. A statistical analysis and verification of the dataset has been carried out and is presented. MMAC was also tested using commercial OCR software and is publicly and freely available.