Abstract
This paper explores the practical implementation and challenges associated with AI and ML in the field of text recognition. It presents a variety of innovative solutions aimed at improving the overall accuracy of text recognition models. These solutions encompass effectively managing data quality and diversity, optimizing large-scale training and inference procedures, providing robust support for multiple languages and fonts, tackling variations in text layout and arrangement, accurately recognizing handwritten text, and enhancing model interpretability and explainability. By addressing these key areas, the proposed solutions aim to significantly enhance the performance and reliability of text recognition systems. As we delve deeper into this investigation, our focus sharpens on the implementation of artificial intelligence and machine learning in the field of text recognition. This paper presents innovative solutions that not only aim to enhance accuracy but also address data quality management, optimize large-scale training, support multilingualism and different fonts, handle layout variations, recognize hand-written text, and improve model interpretability. By addressing these crucial aspects, our proposed solutions have the potential to enhance the overall performance and reliability of text recognition systems, pushing the boundaries of AI and ML applications in this field.
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How to cite this paper
Research on Text Recognition Methods Based on Artificial Intelligence and Machine Learning
How to cite this paper: Fanfei Meng. (2023) Research on Text Recognition Methods Based on Artificial Intelligence and Machine Learning. Advances in Computer and Communication, 4(5), 340-344.
DOI: http://dx.doi.org/10.26855/acc.2023.10.014