View Profile

N Shobha Rani

Associate Professor School of Computing · Amrita vishwa Vidyapeetham

https://researchid.co/shobharani
@amrita.edu
1063Google Scholar Citations
16Google Scholar h-index
37Google Scholar i10-index

Biography

Dr. N. Shobha Rani is an Associate Professor at Department of Computer Science, Amrita Vishwa Vidyapeetham, Mysore. Her research interests are in the field of document digitization, OCR and information capture, plant disease detection and classification and other Computer Vision and deep learning based technologies. She has 10+ years of experience in this field of document image analysis and applications along with teaching experience of 14+ years. Shobha was working as Research Scholar at MIT Mysore. In this role, she has devised and implemented solutions which will contribute to information capture efficiency improvement by OCR along with solutions for pre-processing challenges in pre-printed documents suitable for Govt. organizations. As part of this she has experience in dealing with document images which are structured as well as unstructured, type written & handwritten, dealing with documents with graphical information content. Currently she is guiding six research scholars wit

Education

Ph.D. (June 2012 to January 2017) - Doctorate in Computer Science & Technology from University of Mysore under supervision of Dr. T. Vasudev Title of thesis: An Enhanced Frame Work for Pre-Processing and Character Recognition Systems Suitable for Telugu Documents UGC-NET (National Eligibility Test) Qualified-June 2020 KSET Karnataka State Eligibility Test Assistant Professor Qualified- Dec 2018 M.Sc. (2006-2008) - Master of Science in Computer Science from Sri Venkateswara University, Tirupathi, A.P. B.Sc. (2002-2005) - Bachelor of Science in Maths, Physics and Computer Science from Sri Krishn...

Recent Google Scholar Publications

  1. A hybrid spatial blur detection and restoration algorithm for smartphone captured document images
    Scientific Reports , 2026, 2026
  2. HQA 2 LFS-handwriting quality assessment using an active learning framework in smartphones
    Scientific Reports , 2026, 2026
  3. FeaFusion-PomoNet: A Feature Fusion Driven Regression Framework for Non-Destruction Weight Estimation of Pomegranates
    IEEE Access 13, 213578 - 213599 , 2025, 2025
  4. Plant Region Detection from Infield Images with Unconstrained Backgrounds using U-Net
    IEEE Xplore , 2025, 2025
  5. CISCS: Classification of Inter-Class Similarity based Medicinal Plant Species Groups with Machine Learning
    MethodsX, 103652 , 2025, 2025

Links