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English(EN) Systematic Literature Review of Machine Learning Models and Applications for Text Recognition

AI文献综述详述OCR模型演变与挑战

作者 PulseAugur 编辑部 · [1 个来源] ·

一项近期的文献综述系统性地评估了过去十年光学字符识别（OCR）领域中机器学习模型和应用。该研究遵循PRISMA指南，分析了2015年1月至2025年1月间的97篇精选论文。它追溯了AI模型、应用领域、数据类型和语言覆盖范围的演变，强调了在处理脚本变体、书写风格和退化文档方面的进步。综述还指出了持续存在的挑战，如代表性不足的语言资源有限、手写体变异性以及实时处理限制，并提出了自监督学习和多模态AI等未来方向。 AI

影响 提供了OCR进展的全面概述，指导未来的研究和工业应用。

排序理由 该条目是一篇在arXiv上发表的系统性文献综述论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AI文献综述详述OCR模型演变与挑战

本文如何被排名

Signal score
22 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
该条目是一篇在arXiv上发表的系统性文献综述论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, other
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [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 ·

    机器学习模型和文本识别应用的系统性文献综述

    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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