Kshitij Sharma

A Clinical Decision Support System (CDSS) is a useful support system for healthcare professionals in initial diagnosis of diseases. Symptoms described by patients are key inputs in this decision making process. The proposed work creates a model of commonly occurring Indian diseases along with lab tests identified and medications for the diseases diagnosed. A Bayesian network is automatically generated from the inputs provided by the experts in medical domain which is used to predict the… Show more

A Clinical Decision Support System (CDSS) is a useful support system for healthcare professionals in initial diagnosis of diseases. Symptoms described by patients are key inputs in this decision making process. The proposed work creates a model of commonly occurring Indian diseases along with lab tests identified and medications for the diseases diagnosed. A Bayesian network is automatically generated from the inputs provided by the experts in medical domain which is used to predict the probabilities of possible diseases. The model also has the ability to learn from past experiences.

This project by Amrita Vishwa Vidyapeetham was part of the consultancy work with Paralaxiom Company Pvt. Ltd. Bengaluru, India. Show less

Abstract: Computer Vision and its applications are at the core of industry 4.0. For automating a process, texts embedded in images are considered as good source of information about that object. Reading text from natural images is still a challenging problem because of complicated background, size and space variations, and irregular arrangements of texts. Detection and Recognition are the main stages of reading texts in the wild. In last few years, many researchers have provided various methods… Show more

Abstract: Computer Vision and its applications are at the core of industry 4.0. For automating a process, texts embedded in images are considered as good source of information about that object. Reading text from natural images is still a challenging problem because of complicated background, size and space variations, and irregular arrangements of texts. Detection and Recognition are the main stages of reading texts in the wild. In last few years, many researchers have provided various methods for recognizing texts in images. These methods have achieved fine results on horizontal texts but not on irregular arrangements of texts. This paper primarily focuses on deep learning model for text recognition in images (DL-TRI). The model addresses various cases of horizontal, curved and perspective texts that are hard to recognize.

This project by Amrita Vishwa Vidyapeetham was part of the consultancy work with Paralaxiom Company Pvt. Ltd. Bengaluru, India. Show less