arXiv subjects

Subodh Lonkar

Publications and source records attributed to Subodh Lonkar.

2 recordsLinked to original sources

arXiv2026

Risk-Constrained Evaluation of Financial Document Recognition: A Formal Framework for Evidence, Critical Errors, and Selective Automation

Financial document recognition is commonly evaluated using character and word error rates, which do not adequately represent the consequences of errors in financial workflows. This paper formalizes financial document recognition as a risk-constrained evidence-recovery problem. It defines typed, evidence-linked financial facts and a multi-layer quality model spanning text, layout, tables, fields, semantic relations, calibration, robustness, and operational performance. It introduces non-compensatory acceptance gates based on upper confidence bounds for critical-field errors, and formulates selective automation as a constrained risk-coverage problem with review-capacity limits. The paper also specifies annotation requirements, leakage-resistant benchmark splits, statistical estimands, and a reproducibility card. Formal propositions establish why aggregate scores cannot certify critical-field reliability and why zero observed errors do not imply zero risk. A synthetic example illustrates the decision rule. The work is theoretical; no empirical benchmark or model performance claim is presented.

cs.CV↗

arXiv2021

Facial Expressions Recognition with Convolutional Neural Networks

Over the centuries, humans have developed and acquired a number of ways to communicate. But hardly any of them can be as natural and instinctive as facial expressions. On the other hand, neural networks have taken the world by storm. And no surprises, that the area of Computer Vision and the problem of facial expressions recognitions hasn't remained untouched. Although a wide range of techniques have been applied, achieving extremely high accuracies and preparing highly robust FER systems still remains a challenge due to heterogeneous details in human faces. In this paper, we will be deep diving into implementing a system for recognition of facial expressions (FER) by leveraging neural networks, and more specifically, Convolutional Neural Networks (CNNs). We adopt the fundamental concepts of deep learning and computer vision with various architectures, fine-tune it's hyperparameters and experiment with various optimization methods and demonstrate a state-of-the-art single-network-accuracy of 70.10% on the FER2013 dataset without using any additional training data.

cs.CV↗