Comparing Traditional OCR with Generative AI-Assisted OCR: Advancements and Applications

Dhavalkumar Patel

Abstract: Optical Character Recognition (OCR) has been a transformative technology for decades, enabling the conversion of printed or handwritten text into machine-readable formats. Traditional OCR applications, built on rule-based algorithms and predefined templates, have been instrumental in digitizing structured documents such as invoices, books, and forms. However, these systems often struggle with complex layouts, poor image quality, and handwritten text due to their reliance on rigid frameworks [1]. The advent of Generative Artificial Intelligence (GenAI) has revolutionized OCR technology, addressing the limitations of traditional systems. GenAI-powered OCR leverages advanced neural networks, deep learning, and transformer-based architectures to process diverse document types with remarkable accuracy and adaptability. Unlike traditional OCR, which requires extensive manual configuration, GenAI models can dynamically learn and improve through iterative feedback loops, enabling them to handle complex layouts, varied fonts, and degraded image quality [2][3]. Traditional OCR excels in processing structured and semi-structured documents where layouts are consistent and predictable. However, its inability to adapt to new document types or interpret contextual information limits its effectiveness in modern, dynamic workflows. In contrast, GenAI-assisted OCR introduces capabilities such as contextual understanding, few-shot learning, and multimodal processing, enabling it to extract meaningful insights from unstructured and complex data sources [4][5]. This report explores the key differences between traditional OCR and GenAI-assisted OCR, highlighting their respective strengths, limitations, and applications. By examining the technological advancements and real-world use cases, this comparison aims to provide a comprehensive understanding of how GenAI is reshaping the OCR landscape and unlocking new possibilities for businesses and industries worldwide.

Keywords: Artificial Intelligence, Comparative analysis, Generative AI, Optical Character Recognition, Traditional OCR

How to Cite?: Dhavalkumar Patel, "Comparing Traditional OCR with Generative AI-Assisted OCR: Advancements and Applications", Volume 14 Issue 6, June 2025, International Journal of Science and Research (IJSR), Pages: 347-351, https://www.ijsr.net/getabstract.php?paperid=SR25603211507, DOI: https://dx.doi.org/10.21275/SR25603211507