This work presents the preliminary results for identifying the ancient handwritten writing styles in Arabic manuscripts. The aim of this work is to discriminate between different writing styles appear in different dated ancient Arabic manuscripts in three Islamic historical ages (Contemporary, Ottoman and Mamluk) with objective to evaluate the capability of applying Zipf law on texture structures extracted by Spatial Gray Level dependency (SGLD) method, our approach relies on applying SGLD on handwritten ancient text image to extract the texture structures then applying Zipf’s distribution on document image texture structure. Based on this method we extract a feature presents the document image structure distribution and evaluate its efficiency for writing style identification.

 

Published In : IJCSN Journal Volume 3, Issue 4

Date of Publication : August 2014

Pages : 220 - 224

Figures : 06

Tables : --

Publication Link : Applying Power Law on Texture Structure to Identify the Writing Style in Ancient Manuscripts

 

 

 

Ahmad Abd Al-Aziz : received his M.SC., in computer science from Institute of Statistical Studies and Researches (ISSR), Cairo University, he enrolled in PhD in computer science in (ISSR), Cairo University. He joined Canadian International College (CIC), in 2007 as assistant lecturer. His main areas of research interest are Pattern Recognition, Social Network analysis, Big data, Natural Language processing, sentiment analysis. He had multiple disciplines background: social sciences (since he gained his PhD in behavior sciences from Ain Shams University) and computer science empowered him to scale out his research areas in several topics related to applying information theories in social sciences. Ahmad published two scientific papers in image analysis and pattern recognition.

Mervat Gheith : is assistant professor at Institute of Statistical Studies and Researches (ISSR), Cairo University, her main research interests are artificial intelligence, pattern recognition, image processing and natural language processing. Mervat published several scientific papers in fields of natural language processing and artificial intelligence.

 

 

 

 

 

 

 

Writing Style Identification

Spatial Gray Level Dependency

Power Law

Texture Analysis

This work has been done with objective to discriminate between different writing styles in different Islamic historical ages: Contemporary, Ottoman and Mamluk. We aimed to extend the approach of applying Zipf’’s law in image applications by proposed a new approach based on texture structures generated by SGLM method to identify the writing styles in ancient Arabic manuscripts. This method has an advantage of analyzing the structure of document image patterns instead of image quantization limitations. By applying Zipf’s law on generated SGLD we compute the area under Zipf curve for each writing style and evaluate the experimental results. These results show that applying this method is efficient in identifying different writing styles in different historical Arabic manuscripts. The main disadvantage is that the processing time of SGLD is high in addition the query and stored writing style image should be processed in the same orientation in SGLD matrix generation. In our future work we plan to add more features e.g. curve slope with area under curve which is evaluated in this work in order to increase the efficiency of writing style identification task.

 

 

 

 

 

 

 

 

 

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