publications

2025

1. 

   Health Sentinel: An AI Pipeline For Real-time Disease Outbreak Detection

   Devesh Pant, Rishi Raj Grandhe, Vipin Samaria, and 13 more authors

   2025

   Abs URL

   Early detection of disease outbreaks is crucial to ensure timely intervention by the health authorities. Due to the challenges associated with traditional indicator-based surveillance, monitoring informal sources such as online media has become increasingly popular. However, owing to the number of online articles getting published everyday, manual screening of the articles is impractical. To address this, we propose Health Sentinel. It is a multi-stage information extraction pipeline that uses a combination of ML and non-ML methods to extract events-structured information concerning disease outbreaks or other unusual health events-from online articles. The extracted events are made available to the Media Scanning and Verification Cell (MSVC) at the National Centre for Disease Control (NCDC), Delhi for analysis, interpretation and further dissemination to local agencies for timely intervention. From April 2022 till date, Health Sentinel has processed over 300 million news articles and identified over 95,000 unique health events across India of which over 3,500 events were shortlisted by the public health experts at NCDC as potential outbreaks.

2023

1. 

   Robust OCR Pipeline for Automated Digitization of Mother and Child Protection Cards in India

   Devesh Pant, Dibyendu Talukder, Aaditeshwar Seth, and 7 more authors

   ACM J. Comput. Sustain. Soc., Sep 2023

   Abs DOI

   The Universal Immunization Programme in India has a mandate to fully vaccinate all of India’s 27 million children born annually. The vaccination doses are recorded by frontline health workers on standardized paper-based Mother and Child Protection (MCP) cards, which are manually digitized by data entry operators, resulting in poor data quality, delays, and significant time and resources. In our article, we focus on Optical Character Recognition– (OCR) based automated digitization of MCP card images captured through a smartphone application developed by us. By utilizing a standardized template for the MCP cards, which is available a priori, we register the card images and perform OCR on the extracted region of interest (ROIs). Since the cards with curvature or torn edges had poor ROIs, we built a global–local alignment technique that first approximates the ROI using global homography and then refines using a local homography resulting in improved accuracy. Our pipeline gives a character level accuracy of 98.73% on our dataset against 75.02% by Google Cloud Vision and 79.26% by Azure OCR. We also describe our field testing experience, where the digitized MCP card images were used to provide useful features on the smartphone application for health workers to conduct vaccination sessions.

2022

1. 

   Use of Metric Learning for the Recognition of Handwritten Digits, and its Application to Increase the Outreach of Voice-based Communication Platforms

   Devesh Pant, Dibyendu Talukder, Deepak Kumar, and 3 more authors

   In Proceedings of the 5th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, Seattle, WA, USA, Sep 2022

   Abs DOI

   Initiation, monitoring, and evaluation of development programmes can involve field-based data collection about project activities. This data collection through digital devices may not always be feasible though, for reasons such as unaffordability of smartphones and tablets by field-based cadre, or shortfalls in their training and capacity building. Paper-based data collection has been argued to be more appropriate in several contexts, with automated digitization of the paper forms through OCR (Optical Character Recognition) and OMR (Optical Mark Recognition) techniques. We contribute with providing a large dataset of handwritten digits, and deep learning based models and methods built using this data, that are effective in real-world environments. We demonstrate the deployment of these tools in the context of a maternal and child health and nutrition awareness project, which uses IVR (Interactive Voice Response) systems to provide awareness information to rural women SHG (Self Help Group) members in north India. Paper forms were used to collect phone numbers of the SHG members at scale, which were digitized using the OCR tools developed by us, and used to push almost 4 million phone calls. The data, model, and code have been released in the open-source domain.