Name: MMAC
Description: 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.”
Provider: Multiple Institutions
Paper Title: Building a multi-modal Arabic corpus (MMAC)
Tasks: dictionary 
Venue Name: International Journal on Document Analysis and Recognition 
Authors: Ashraf AbdelRaouf, Colin A. Higgins, Tony Pridmore,  Mahmoud Khalil
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.