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

arXiv: https://arxiv.org/abs/2608.26500 · HTML: https://mlsanity.com/paper/2608.26500/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 171 references that are in the map.
- First posted on arXiv 27 Aug 2026.

## Builds on (most cited first, 22 of 22)

- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models](https://mlsanity.com/paper/2406.14852/) (2024)
- [From Matching to Generation: A Survey on Generative Information Retrieval](https://mlsanity.com/paper/2404.14851/) (2024)
- [Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)](https://mlsanity.com/paper/2001.00139/) (2020)
- [DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement](https://mlsanity.com/paper/2010.08764/) (2020)
- [Systematic Literature Review: Quantum Machine Learning and its applications](https://mlsanity.com/paper/2201.04093/) (2022)
- [Meta-learning approaches for few-shot learning: A survey of recent advances](https://mlsanity.com/paper/2303.07502/) (2023)
- [A Survey of Historical Document Image Datasets](https://mlsanity.com/paper/2203.08504/) (2022)
- [CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising](https://mlsanity.com/paper/2207.07798/) (2022)
- [Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence](https://mlsanity.com/paper/2106.08710/) (2021)
- [Information Extraction from Scanned Invoice Images using Text Analysis and Layout Features](https://mlsanity.com/paper/2208.04011/) (2022)
- [Extending TrOCR for Text Localization-Free OCR of Full-Page Scanned Receipt Images](https://mlsanity.com/paper/2212.05525/) (2022)
- [A data filling methodology for time series based on CNN and (Bi)LSTM neural networks](https://mlsanity.com/paper/2204.09994/) (2022)
- [A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing](https://mlsanity.com/paper/2307.04245/) (2023)
- [BLPnet: A new DNN model and Bengali OCR engine for Automatic License Plate Recognition](https://mlsanity.com/paper/2202.12250/) (2022)
- [Npix2Cpix: A GAN-Based Image-to-Image Translation Network With Retrieval- Classification Integration for Watermark Retrieval From Historical Document Images](https://mlsanity.com/paper/2406.03556/) (2024)
- [Indonesian ID Card Extractor Using Optical Character Recognition and Natural Language Post-Processing](https://mlsanity.com/paper/2101.05214/) (2020)
- [Chapter 7 Review of Data-Driven Generative AI Models for Knowledge Extraction from Scientific Literature in Healthcare](https://mlsanity.com/paper/2411.11635/) (2024)

## Abstract (arXiv)

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 technology are leading to new AI models with improved architecture for handling multiple languages and complex data formats. Despite this progress, a comprehensive evaluation of OCR advancements remains limited. Based on the established preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines, this literature review presents an extensive assessment of OCR research to trace the evolution of AI models over the past decade. It explores the transition in AI models, application domains, data types, linguistic coverage, and challenges. Through a detailed analysis of 97 selected studies published during January 2015 - January 2025, key OCR models are identified, and their performance, strengths, and limitations are analyzed. The findings highlight how OCR technologies have evolved to address structured and unstructured text, scene text recognition, and multilingual processing. Unresolved challenges include limited resources for underrepresented languages, high variability in handwritten text, visual similarity among characters, and constraints in real-time OCR applications. To address these issues, several promising approaches are proposed. Key suggestions include self-supervised learning, multimodal AI, automated machine learning (AutoML), AI-assisted postprocessing, tiny machine learning (TinyML), and the creation of joint corpora for script matching. The future recommendations aim to enhance OCR accuracy and tackle the challenges identified for real-time industrial applications. This study will guide future research and establish a foundation for OCR field.