Assessing GPT Model Uncertainty in Mathematical OCR Tasks via Entropy Analysis

TL;DR

This paper explores how GPT models' uncertainty in recognizing mathematical equations from images varies with image quality, using entropy analysis to quantify confidence and improve OCR accuracy.

Contribution

It introduces an entropy-based framework to assess GPT model uncertainty in mathematical OCR, linking image resolution to recognition confidence.

Findings

Higher image resolution reduces GPT uncertainty and improves accuracy.

Entropy correlates with recognition errors, serving as a confidence measure.

Practical insights for optimizing image quality in GPT-based OCR applications.

Abstract

This paper investigates the uncertainty of Generative Pre-trained Transformer (GPT) models in extracting mathematical equations from images of varying resolutions and converting them into LaTeX code. We employ concepts of entropy and mutual information to examine the recognition process and assess the model's uncertainty in this Optical Character Recognition (OCR) task. By analyzing the conditional entropy of the output token sequences, we provide both theoretical insights and practical measurements of the GPT model's performance given different image qualities. Our experimental results, obtained using a Python implementation available on GitHub, demonstrate a clear relationship between image resolution and GPT model uncertainty. Higher-resolution images lead to lower entropy values, indicating reduced uncertainty and improved accuracy in the recognized LaTeX code. Conversely, lower-resolution images result in increased entropy, reflecting higher uncertainty and a higher likelihood of recognition errors. These findings highlight the practical importance of considering image quality in GPT-based mathematical OCR applications and demonstrate how entropy analysis, grounded in information-theoretic concepts, can effectively quantify model uncertainty in real-world tasks.