Pragati Chandankhede

Work place: SPSU, Dept of Computer Engineering, 313001, India



Research Interests: Natural Language Processing, Artificial Intelligence, Software Engineering, Computing Platform


Pragati Chandankhede is PhD scholar from Sir Padampat Singhania University Rajasthan. She has completed her MTech from G.H.Raisoni College of Engineering in Year 2013. She has been Engineer in stream of Information technology in year 2009 from JDIET, Yavatmal. Pragati Chandankhede is Assistant Professor at KCCEMSR, Thane. She has 10 years of experience in teaching. Her area of interest includes artificial intelligence, Natural Language Processing, soft computing, software Engineering. She is PhD Pursuing from Sir Padampat Singhania University Rajasthan. Her PhD works mainly focuses on Computer vision and digitization of real world images. Her recent published article was titled “Using Machine Learning for Image Recommendation in News Articles” published in Recent Advances in Artificial Intelligence and Data Engineering pp 215-225 on November2021. Another published article was titled “Gesture Based Media Controlling using Haar Cascade” published in International Conference on Innovative Computing and Communications pp 541-551 on August 2021. Another published article titled “Deep Learning Technique for serving Visually Impaired Person” in IEEE Xplore on 14 May 2020. Pragati Chandankhede is member of Professional Society of IETE and ISTE.

Author Articles
Guiding Aid for Visually Impaired

By Pragati Chandankhede Arun Kumar

DOI:, Pub. Date: 8 Apr. 2022

Visual impairment is where the person either can’t see or his vision has weakened to large extent. There is no alternative technique for visually impairment, but to some extent it can be trim down with devices, smart sticks and sensors. Although many techniques are there for helping out through electronic travelling aid, cost effective and minimum hardware solution was the expectation by impaired. The device which can identify and classify the object ahead of impaired person is needed so that person can be prevented from the accident. In this paper, a unified model of YOLO (You Only Look Once) is used for detection of object ahead of camera. The proposed model is based on phenomena of detecting small object and good detection speed of yolov3 makes system more robust. Once detected, labeled objects name is converted from text to speech, so that blind person can be alerted from colliding with obstacles. This paper is one step in the direction to help them by exactly classifying, detecting and localizing target object along with providing voice based guideline. The proposed model has proved accuracy in many real time scenes.

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