Rumaan Bashir

Work place: Department of Computer Science, Islamic University of Science & Technology, Awantipora, Pulwama, J&K, 192122, India



Research Interests: Image Processing, Image Manipulation, Image Compression


Ms. Rumaan Bashir is working as Assistant Professor in the Department of Computer Science, Islamic University of Science and Technology, Awantipora, J&K, India. Her area of research is Pattern Recognition/Document Image Analysis and is presently working on Script Identification. The author received her MCA and PGDBA from the University of Kashmir, J&K, India. She has done M.Phil in Computer Science and is currently pursuing her Ph. D from University of Kashmir, J&K, India.

Author Articles
Density Based Script Identification of a Multilingual Document Image

By Rumaan Bashir S.M.K. Quadri

DOI:, Pub. Date: 8 Jan. 2015

Automatic Pattern Recognition field has witnessed enormous growth in the past few decades. Being an essential element of Pattern Recognition, Document Image Analysis is the procedure of analyzing a document image with the intention of working out the contents so that they can be manipulated as per the requirements at various levels. It involves various procedures like document classification, organizing, conversion, identification and many more. Since a document chiefly contains text, Script Identification has grown to be a very important area of this field. A Script comprises the text of a document or a manuscript. It is a scheme of written characters and symbols used to write a particular language. Languages are written using scripts, but script itself is made up of symbols. Every language has its own set of symbols used for writing it. Sometimes different languages are written using the same script, but with marginal modification. Script Identification has been performed for unilingual, bilingual and multilingual document images. But, negligible work has been reported for Kashmiri script. In this paper, we are analyzing and experimentally testing statistical approach for identification of Kashmiri script in a document image along with Roman, Devanagari & Urdu scripts. The identification is performed on offline machine-printed scripts and yields promising results.

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