Showing papers by "Gaurav Harit published in 2019"

An Unsupervised Sequence-to-Sequence Autoencoder Based Human Action Scoring Model

Abstract: Developing a model for the task of assessing quality of human action is a key research area in computer vision. The quality assessment task has been posed as a supervised regression problem, where models have been trained to predict score, given action representation features. However, human proficiency levels can widely vary and so do their scores. Providing all such performance variations and their respective scores is an expensive solution as it requires a domain expert to annotate many videos. The question arises - Can we exploit the variations of the performances from that of expert and map the variations to their respective scores? To this end, we introduce a novel sequence-to-sequence autoencoder-based scoring model which learns the representation from only expert performances and judges an unknown performance based on how well it can be regenerated from the learned model. We evaluated our model in predicting scores of a complex Sun- Salutation action sequence, and demonstrate that our model gives remarkable prediction accuracy compared to the baselines.

DocDescribor: Digits + Alphabets + Math Symbols - A Complete OCR for Handwritten Documents

Abstract: This paper presents an Optical Character Recognition (OCR) system for documents with English text and mathematical expressions. Neural network architectures using CNN layers and/or dense layers achieve high level accuracy in character recognition task. However, these models require large amount of data to train the network, with balanced number of samples for each class. Recognition of mathematical symbols poses challenges of the imbalance and paucity of training data available. To address this issue, we pose the character recognition problem as a Distance Metric Learning problem. We propose a Siamese-CNN Network that learns discriminative features to identify if the two images in a pair contain similar or dissimilar characters. The network is then used to recognize different characters by character matching where test images are compared to sample images of any target class which may or may not be included during training. Thus our model can scale to new symbols easily. The proposed approach is invariant to author’s handwriting. Our model has been tested over images extracted from a dataset of scanned answer scripts collected by us. It is seen that our approach achieves comparable performance to other architectures using convolutional layers or dense layers while using lesser training data.