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AI models for text recognition reviewed: challenges and future directions

By PulseAugur Editorial · [2 sources] ·

A recent literature review, adhering to PRISMA guidelines, analyzes 97 studies from January 2015 to January 2025 on machine learning models for optical character recognition (OCR). The review details the evolution of AI models in OCR, their application domains, and linguistic coverage, highlighting advancements in handling varied scripts and degraded documents. It also identifies persistent challenges such as limited resources for underrepresented languages and variability in handwritten text, proposing solutions like self-supervised learning, multimodal AI, and TinyML for future improvements. AI

IMPACT Provides a foundational overview and future recommendations for OCR research, guiding development in areas like multilingual processing and real-time applications.

RANK_REASON The cluster consists of a systematic literature review paper on AI models for text recognition.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI models for text recognition reviewed: challenges and future directions

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The cluster consists of a systematic literature review paper on AI models for text recognition.
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COVERAGE [2]

  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…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    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 styles, and degraded documents. Advancements in technol…

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