MMAC
The multi-modal Arabic corpus contains 6 million Arabic words selected from various sources covering old Arabic, religious texts, traditional language, modern language, different specialisations and very modern material from online “chat rooms.”
Multiple Institutions
Building a multi-modal Arabic corpus (MMAC)
dictionary 
International Journal on Document Analysis and Recognition 
Ashraf AbdelRaouf, Colin A. Higgins, Tony Pridmore,  Mahmoud Khalil
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.