Projects
Generative Multimodal Learning for Reconstructing Missing Modality
Training a latent variable based variational inference model on multimodal data in order to perform inference with all possible combinations of missing modalities.
Highlighter(Auto field detection)
A tool to highlight/extract specific form fields from documents using classical Computer Vision and heuristics
Policy Gradient
Reproducibility and Analysis of Deep Policy Gradient methods for Reinforcement Learning Tasks
Online Learning of temporal Knowledge Graphs
In this project, we apply currently available solutions to address incremental knowledge graph embedding to several applications to test their efficiency.
Generative Adversarial Networks: Reproducibility Study
A reproducibility test, ablation studies and extension of the seminal Generative Adversarial Networks paper
Incremental Knowledge Graphs
In this project, we propose an incremental learning problem for Knowledge Graphs to obtain representations for new entities and also update the representations of old entities that share facts with these newer entities.
Image Stitching (Panorama)
Implemented an image stitching algorithm for creating panoramas from successive images from a rotating camera from scratch.
Modified MNIST [Kaggle]
Identifying the highest number present in modified MNIST images containing multiple handwritten digits on random backgrounds using deep learning
SIFT
Implementing Scale Invariant Feature Transform from scratch and feature matching
Reddit Comment Classification [Kaggle]
We analyze different Machine Learning models to process Reddit data and develop a supervised classification model that can predict what community a certain comment came from.
Generic Extraction Module (G.E.M)
Trained a biLSTM model using both word and character level embeddings for information retrieval from text OCR outputs of ID cards
Image Quality Assessment
An ensemble model to quantify image quality to filter poor quality images at the client end to prevent redundant processing
Dory OCR
We created a state of the art Optical Character Recognition Engine specifically for Indian ID cards using a pipeline for document layout detection, foreground extraction, text detection, recognition and postprocessing
Sign Language Classification [Bachelor Project]
Deep Learning based Indian Sign Language detection for conversion to speech, a subsystem of the Hindi speech-Indian sign language interconversion system
Cropnet
Regression based deep learning models for automatically cropping document as foreground extraction(segmentation) task
March Madness [Kaggle]
Applying Machine Learning to March Madness College Basketball tournament for predicting tournament match results
Activity Recognition
Using traditional computer vision with deep learning algorithms for Anomalous activity detection from CCTV camera feed
OCR to enrich ASR
Optical Character Recognition in Lecture Videos for the enrichment of Automatic Speech Recognition(ASR) system
Speaker Recognition
Comparative analysis of Neural Network performances for the task of Speaker Recognition using Hindi Digit database in clean and noisy environment
Multilingual Speech Recognition
Hindi and English digit recognition using MFCC features and five different neural networks and their performance evaluation under different conditions
