Can AI Assistance Aid in the Grading of Handwritten Answer Sheets?

With recent advancements in artificial intelligence (AI), there has been growing interest in using state of the art (SOTA) AI solutions to provide assistance in grading handwritten answer sheets. While a few commercial products exist, the question of whether AI-assistance can actually reduce grading effort and time has not yet been carefully considered in published literature. This work introduces an AI-assisted grading pipeline. The pipeline first uses text detection to automatically detect question regions present in a question paper PDF. Next, it uses SOTA text detection methods to highlight important keywords present in the handwritten answer regions of scanned answer sheets to assist in the grading process. We then evaluate a prototype implementation of the AI-assisted grading pipeline deployed on an existing e-learning management platform. The evaluation involves a total of 5 different real-life examinations across 4 different courses at a reputed institute; it consists of a total of 42 questions, 17 graders, and 468 submissions. We log and analyze the grading time for each handwritten answer while using AI assistance and without it. Our evaluations have shown that, on average, the graders take 31% less time while grading a single response and 33% less grading time while grading a single answer sheet using AI assistance.

TEXTRON: Weakly Supervised Multilingual Text Detection through Data Programming

Several recent deep learning (DL) based techniques perform considerably well on image-based multilingual text detection. However, their performance relies heavily on the availability and quality of training data. There are numerous types of page-level document images consisting of information in several modalities, languages, fonts, and layouts. This makes text detection a challenging problem in the field of computer vision (CV), especially for low-resource or handwritten languages. Furthermore, there is a scarcity of word-level labeled data for text detection, especially for multilingual settings and Indian scripts that incorporate both printed and handwritten text. Conventionally, Indian script text detection requires training a DL model on plenty of labeled data, but to the best of our knowledge, no relevant datasets are available. Manual annotation of such data requires a lot of time, effort, and expertise. In order to solve this problem, we propose TEXTRON, a Data Programming-based approach, where users can plug various text detection methods into a weak supervision-based learning framework. One can view this approach to multilingual text detection as an ensemble of different CV-based techniques and DL approaches. TEXTRON can leverage the predictions of DL models pre-trained on a significant amount of language data in conjunction with CV-based methods to improve text detection in other languages. We demonstrate that TEXTRON can improve the detection performance for documents written in Indian languages, despite the absence of corresponding labeled data. Further, through extensive experimentation, we show improvement brought about by our approach over the current State-of-the-art (SOTA) models, especially for handwritten Devanagari text. Code and dataset has been made available at https://github.com/IITB-LEAP-OCR/TEXTRON

A Deep Recurrent Framework for Cleaning Motion Capture Data

We present a deep, bidirectional, recurrent framework for cleaning noisy and incomplete motion capture data. It exploits temporal coherence and joint correlations to infer adaptive filters for each joint in each frame. A single model can be trained to denoise a heterogeneous mix of action types, under substantial amounts of noise. A signal that has both noise and gaps is preprocessed with a second bidirectional network that synthesizes missing frames from surrounding context. The approach handles a wide variety of noise types and long gaps, does not rely on knowledge of the noise distribution, and operates in a streaming setting. We validate our approach through extensive evaluations on noise both in joint angles and in joint positions, and show that it improves upon various alternatives.