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AI literature review details OCR model evolution and challenges

By PulseAugur Editorial · [1 sources] ·

A recent literature review systematically evaluated machine learning models and applications in Optical Character Recognition (OCR) over the past decade. The study, adhering to PRISMA guidelines, analyzed 97 selected papers from January 2015 to January 2025. It traced the evolution of AI models, application domains, data types, and linguistic coverage, highlighting advancements in handling script variations, writing styles, and degraded documents. The review also identified persistent challenges such as limited resources for underrepresented languages, variability in handwriting, and real-time processing constraints, proposing future directions like self-supervised learning and multimodal AI. AI

IMPACT Provides a comprehensive overview of OCR advancements, guiding future research and industrial applications.

RANK_REASON The item is a systematic literature review paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI literature review details OCR model evolution and challenges

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The item is a systematic literature review paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Nuzhat Khan, Ab Al-Hadi Ab Rahman, Shahriyar Masud Rizvi, Ibrahim Yousef Alshareef, Muhammad Nadzir Marsono, Muhammad Paend Bakht, Mohd Shahrizal Rusli, Shahidatul Sadiah ·

    Systematic Literature Review of Machine Learning Models and Applications for Text Recognition

    arXiv:2608.26500v1 Announce Type: cross Abstract: Optical Character Recognition (OCR) for text recognition using machine vision has significantly improved, particularly when handling heterogeneous textual data. Traditional OCR models struggle with script variations, writing style…

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