AEJAZ FAROOQ GANAI
@nitsri.ac.in
LECTURER IN THE DEPARTMENT OF COMPUTER ENGINEERING(Technical Education Department)
PH.D from NATIONAL INSTITUTE OF TECHNOLOGY SRINAGAR
Dr. Aejaz Farooq Ganai
Address: Srinagar, Kashmir Jammu and Kashmir, India
Ph.D. in pattern recognition using deep learning at the National Institute of Technology, Srinagar, India.
Master of Technology in Computer Engineering from SMVDU Katra Jammu
Bachelor of Engineering in Computer Engineering from University of Kashmir Srinagar
EDUCATION
Doctor of Philosophy in Pattern Recognition using Deep Learning
M.Tech. in Computer Engineering
B.E in Computer Engineering
Qualified GATE 2013 in CS&IT
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Science
FUTURE PROJECTS
Digitization of All written Records of J&K Government
Digitization of All written Records of J&K Government. if interested to be a part of Project, For further details, please contact me my email.
Applications Invited
Collaborator/student
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Aejaz Farooq Ganai and Farida Khursheed
Springer Science and Business Media LLC
Aejaz Farooq Ganai and Farida Khursheed
Springer Science and Business Media LLC
Aejaz Farooq Ganai and Farida Khursheed
IEEE
Language Modeling is defined as the operation of predicting next word. It is considered as one of the basic tasks of Natural Language Processing(NLP) and Language Modeling has several applications. In this research paper, the assorted potentialities for the efficient utilization of language models in structured document retrieval are mentioned. A tree-based generative language model for ranking documents and parts has been used here. Nodes within the tree correspond to different document parts like titles, paragraphs and sections. At every node within the document tree, there's a well-defined language model. The language model for a leaf node is predictable directly from the text within the document part related to the node. Inner nodes within the tree are predictable employing a linear interpolation among the various youngster nodes. The paper additionally describes how some common structural queries would be satisfactorily described inside this model.
Aejaz Farooq Ganai and Faisal Rasheed Lone
IEEE
Urdu Nastaleeq is a highly cursive, context sensitive language, written diagonally from top right to bottom left that makes it difficult to segment the partial word or a compete word into characters. Further due to stacking of characters, the segmentation at the character level is hard to perform. Some researchers have performed the ligature level segmentation and have succeeded to a great extent, but the accuracy of segmentation is still less and needs to improved. In this paper, the methodology for segmentation of Urdu nastaleeq at the character level is presented. The various challenges encountered during segmentation have been discussed in detail.
Aejaz Farooq Ganai and Ajay Koul
IEEE
Urdu Nastaleeq is a highly cursive, context sensitive language, written diagonally from top right to bottom left. This makes it difficult to segment the partial word or a complete word into characters. Further due to stacking of characters, the segmentation at the character level is hard to perform. Some researchers have performed the segmentation and have succeeded to a good extent, but still some classes of Urdu alphabets have not been recognized yet. Moreover accuracy of segmentation is still less and has to be improved to develop Optical Character Recognition. We present a Novel approach of Projection Profile methodology for segmentation of Urdu Nastaleeq Ligature. The various challenges encountered during segmentation have been discussed in detail.