I am Data Scientist at Monsoon CreditTech. Where we helps lenders leverage the power of machine learning via our proprietary loan-underwriting platform to reduce delinquency rates, increase approval rates and boost loan-loss adjusted. Before Monsoon CreditTech, I have also completed worked as a ML/DL intern in three startup companies.
I have published two international research papers both in the AI/ML/DL domain, one of which got published at the CVPR conference. CVPR is the topmost computer science conference in the world. The Github code repository for the same paper has 960+ stars and 300+ forks.
PICT is a well-known college in Pune. I completed my B.E. in Information Technology. During my tenure at PICT, I completed four internships in the field of ML and DL, published two international research papers and had been two times smart India hackathon finalist.
Gondia is a small town near Nagpur, I have spent my 3 years here learning the basics of computer science, Explored various domains such as web and android development, Cyber Security, UI/UX and game development. I also got the opportunity to lead various projects.
CascadTabNet is an automatic table recognition method for interpretation of tabular data in document images. We present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model. CascadeTabNet is a Cascade mask Region-based CNN High-Resolution Network (Cascade mask R-CNN HRNet) based model that detects the regions of tables and recognizes the structural body cells from the detected tables at the same time. We evaluate our results on ICDAR 2013, ICDAR 2019 and TableBank public datasets. We achieved 3rd rank in ICDAR 2019 post-competition results for table detection while attaining the best accuracy results for the ICDAR 2013 and TableBank dataset. We also attain the highest accuracy results on the ICDAR 2019 table structure recognition dataset
Pre-print More and CodeWe propose an efficient way to estimate the traffic density on intersection using image processing and machine learning techniques in real time. The proposed methodology takes pictures of traffic at junction to estimate the traffic density. We use Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Support Vector Machine (SVM) based approach for traffic density estimation. The strategy is computationally inexpensive and might run efficiently on raspberry pi board
Pre-print More and Code
Android App for registration of participants in Enthusia Event
Scans the vehicle number plate, and extracts information about the owner of the vehicle