Artificial Intelligence and Machine Learning Approaches to Text Recognition: A Research Overview

Abstract

Fanfei Meng and Chen-Ao Wang

This manuscript explores the application and inherent challenges of artificial intelligence (AI) and machine learning (ML) within the context of text recognition. It proposes a suite of innovative methodologies designed to significantly augment the accuracy of text recognition models. These methodologies encompass strategies for enhancing data quality and diversity, optimizing processes for large-scale training and inference, offering comprehensive support for a multitude of languages and typographies, addressing variations in text layout and configurations, achieving precise recognition of handwritten text, and enhancing the interpretability and explainability of models. Through addressing these pivotal areas, the proposed solutions endeavor to markedly improve the efficacy and reliability of text recognition systems. This investigation provides a focused examination of the integration of AI and ML technologies in text recognition, presenting solutions that not only aim at augmenting accuracy but also at resolving critical challenges related to data quality management, scalability of training protocols, support for multilingualism and diverse fonts, adaptability to text layout variations, recognition of handwritten texts, and model transparency. By concentrating on these essential factors, the proposed approaches seek to advance the overall performance and reliability of text recognition systems, thereby extending the frontiers of AI and ML implementations in this domain.

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