AEJAZ FAROOQ GANAI

@nitsri.ac.in

LECTURER IN THE DEPARTMENT OF COMPUTER ENGINEERING(Technical Education Department)
PH.D from NATIONAL INSTITUTE OF TECHNOLOGY SRINAGAR

AEJAZ FAROOQ GANAI


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https://researchid.co/aejazfarooq
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
9

Scopus Publications

113

Scholar Citations

6

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • Multilingual OCR for cursive scripts using Swin Transformers
    Aejaz Farooq Ganai, Nasir N. Hurrah
    Neural Computing and Applications, 2026
  • Towards automatic efficient recognition of multiresolution offline handwritten Urdu text using swin transformers
    Aejaz Farooq Ganai, Nasir N. Hurrah, Farida Khursheed
    International Journal on Document Analysis and Recognition, 2026
  • Computationally efficient recognition of unconstrained handwritten Urdu script using BERT with vision transformers
    Aejaz Farooq Ganai, Farida Khursheed
    Neural Computing and Applications, 2023
  • Handwritten urdu recognition using BERT with vision transformers
    Aejaz Farooq Ganai, Farida Khurshid
    Intelligent Multimedia Signal Processing for Smart Ecosystems, 2023
  • Computationally Efficient Holistic Approach for Handwritten Urdu Recognition using LRCN Model.
    International Journal of Intelligent Systems and Applications in Engineering, 2023
  • A novel holistic unconstrained handwritten urdu recognition system using convolutional neural networks
    Aejaz Farooq Ganai, Farida Khursheed
    International Journal on Document Analysis and Recognition, 2022
  • Predicting next Word using RNN and LSTM cells: Stastical Language Modeling
    Aejaz Farooq Ganai, Farida Khursheed
    Proceedings of the IEEE International Conference Image Information Processing, 2019
    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.
  • Character segmentation for Nastaleeq URDU OCR: A review
    Aejaz Farooq Ganai, Faisal Rasheed Lone
    International Conference on Electrical Electronics and Optimization Techniques Iceeot 2016, 2016
    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.
  • Projection profile based ligature segmentation of Nastaleeq Urdu OCR
    Aejaz Farooq Ganai, Ajay Koul
    2016 4th International Symposium on Computational and Business Intelligence Iscbi 2016, 2016
    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.

RECENT SCHOLAR PUBLICATIONS

  • Multilingual OCR for cursive scripts using Swin Transformers
    AF Ganai, NN Hurrah
    Neural Computing and Applications 38 (9), 345 , 2026
    2026.0
  • Towards automatic efficient recognition of multiresolution offline handwritten Urdu text using swin transformers
    AF Ganai, NN Hurrah, F Khursheed
    International Journal on Document Analysis and Recognition (IJDAR), 1-18 , 2025
    2025.0
    Citations: 1
  • Computationally efficient recognition of unconstrained handwritten Urdu script using BERT with vision transformers
    AF Ganai, F Khursheed
    Neural Computing and Applications 35 (34), 24161-24177 , 2023
    2023.0
    Citations: 13
  • Handwritten urdu recognition using BERT with vision transformers
    AF Ganai, F Khurshid
    Intelligent Multimedia Signal Processing for Smart Ecosystems, 351-366 , 2023
    2023.0
    Citations: 3
  • Computationally efficient holistic approach for handwritten urdu recognition using lrcn model
    AF Ganai, F Khursheed
    Int. J. Intell. Syst. Appl. Eng 11, 536-551 , 2023
    2023.0
    Citations: 6
  • A novel holistic unconstrained handwritten urdu recognition system using convolutional neural networks
    AF Ganai, F Khursheed
    International Journal on Document Analysis and Recognition (IJDAR) 25 (4 … , 2022
    2022.0
    Citations: 13
  • Predicting next word using RNN and LSTM cells: Stastical language modeling
    AF Ganai, F Khursheed
    2019 fifth international conference on image information processing (ICIIP … , 2019
    2019.0
    Citations: 55
  • Projection profile based ligature segmentation of Nastaleeq Urdu OCR
    AF Ganai, A Koul
    2016 4th International Symposium on Computational and Business Intelligence … , 2016
    2016.0
    Citations: 13
  • Character segmentation for Nastaleeq URDU OCR: a review
    AF Ganai, FR Lone
    2016 international conference on electrical, electronics, and optimization … , 2016
    2016.0
    Citations: 9
  • Raymond Wong 153, 159 Rolf Dornberger 59, 66, 112, 118, 125 Rui Tang 153, 159 Sara Hosseini 191 Shrawan Kumar Trivedi 176
    AD Dileep, A Wlodarczyk, AF Ganai, A Koul, AR Kashani, AH Gandomi, ...

MOST CITED SCHOLAR PUBLICATIONS

  • Predicting next word using RNN and LSTM cells: Stastical language modeling
    AF Ganai, F Khursheed
    2019 fifth international conference on image information processing (ICIIP … , 2019
    2019.0
    Citations: 55
  • Computationally efficient recognition of unconstrained handwritten Urdu script using BERT with vision transformers
    AF Ganai, F Khursheed
    Neural Computing and Applications 35 (34), 24161-24177 , 2023
    2023.0
    Citations: 13
  • A novel holistic unconstrained handwritten urdu recognition system using convolutional neural networks
    AF Ganai, F Khursheed
    International Journal on Document Analysis and Recognition (IJDAR) 25 (4 … , 2022
    2022.0
    Citations: 13
  • Projection profile based ligature segmentation of Nastaleeq Urdu OCR
    AF Ganai, A Koul
    2016 4th International Symposium on Computational and Business Intelligence … , 2016
    2016.0
    Citations: 13
  • Character segmentation for Nastaleeq URDU OCR: a review
    AF Ganai, FR Lone
    2016 international conference on electrical, electronics, and optimization … , 2016
    2016.0
    Citations: 9
  • Computationally efficient holistic approach for handwritten urdu recognition using lrcn model
    AF Ganai, F Khursheed
    Int. J. Intell. Syst. Appl. Eng 11, 536-551 , 2023
    2023.0
    Citations: 6
  • Handwritten urdu recognition using BERT with vision transformers
    AF Ganai, F Khurshid
    Intelligent Multimedia Signal Processing for Smart Ecosystems, 351-366 , 2023
    2023.0
    Citations: 3
  • Towards automatic efficient recognition of multiresolution offline handwritten Urdu text using swin transformers
    AF Ganai, NN Hurrah, F Khursheed
    International Journal on Document Analysis and Recognition (IJDAR), 1-18 , 2025
    2025.0
    Citations: 1
  • Multilingual OCR for cursive scripts using Swin Transformers
    AF Ganai, NN Hurrah
    Neural Computing and Applications 38 (9), 345 , 2026
    2026.0
  • Raymond Wong 153, 159 Rolf Dornberger 59, 66, 112, 118, 125 Rui Tang 153, 159 Sara Hosseini 191 Shrawan Kumar Trivedi 176
    AD Dileep, A Wlodarczyk, AF Ganai, A Koul, AR Kashani, AH Gandomi, ...