10 Awesome OCR Models for 2025

Stay ahead in 2025 with the latest OCR models optimized for speed, accuracy, and versatility in handling everything from scanned documents to complex layouts.

By Kanwal Mehreen, KDnuggets Technical Editor & Content Specialist on June 6, 2025 in Machine Learning

Image by Author | Canva

OCR models have come a long way. What used to be slow, glitchy, and barely usable tools have now turned into fast, accurate systems that can read just about anything from handwritten notes to multi-language PDFs. If you're working with unstructured data, building automations, or setting up anything that involves scanned documents or images with text, OCR is key.

You’re probably already familiar with the usual names like Tesseract, EasyOCR, PaddleOCR, and maybe Google Vision. They’ve been around for a while and have done the job. But honestly, 2025 feels different. Today’s OCR models are faster, more accurate, and capable of handling much more complex tasks like real-time scene text recognition, multilingual parsing, and large-scale document classification.

I’ve done the research to bring you a list of the best OCR models you should be using in 2025. This list is sourced from GitHub, research papers, and industry updates covering both open-source and commercial options. So, let’s get started.

1. MiniCPM-o

Link: https://huggingface.co/openbmb/MiniCPM-o-2_6
MiniCPM-o has been one of the most impressive OCR models I’ve come across recently. Developed by OpenBMB, this lightweight model (only 8B parameters) can process images with any aspect ratio up to 1.8 million pixels. This makes it ideal for high-resolution document scanning. It currently tops the OCRBench leaderboard with version 2.6. That’s higher than some of the biggest names in the game, including GPT-4o, GPT-4V, and Gemini 1.5 Pro. It also has support for over 30 languages. Another thing I love about it is the efficient token usage (640 tokens for a 1.8MP image), making it not only fast but also perfect for mobile or edge deployments.

2. InternVL

Link: https://github.com/OpenGVLab/InternVL
InternVL is a powerful open-source OCR and vision-language model developed by OpenGVLab. It's a strong alternative to closed models like GPT-4V, especially for tasks like document understanding, scene text recognition, and multimodal analysis. InternVL 2.0 can handle high-resolution images (up to 4K) by breaking them into smaller 448x448 tiles, making it efficient for large documents. It also got an 8k context window, which means it can handle longer and more complex documents with ease. InternVL 3 is the latest in the series and takes things even further. It’s not just about OCR anymore—this version expands into tool use, 3D vision, GUI agents, and even industrial image analysis.

3. Mistral OCR

Link: https://mistral.ai/news/mistral-ocr
Mistral OCR launched in early 2025 and has quickly become one of the most reliable tools for document understanding. Built by Mistral AI, the API works well with complex documents like PDFs, scanned images, tables, and equations. It accurately extracts text and visuals together, making it useful for RAG. . It supports multiple languages and outputs results in formats like markdown, which helps keep the structure clear. Pricing starts at $1 per 1,000 pages, with batch processing offering better value. The recent mistral-ocr-2505 update improved its performance on handwriting and tables, making it a strong choice for anyone working with detailed or mixed-format documents.

4. Qwen2-VL

Link: https://github.com/QwenLM
Qwen2-VL, part of Alibaba’s Qwen series, is a powerful open-source vision-language model that I’ve found incredibly useful for OCR tasks in 2025. It’s available in several sizes, including 2B, 7B, and 72B parameters, and supports over 90 languages. The 2.5-VL version performs really well on benchmarks like DocVQA and MathVista, and even comes close to GPT-4o in accuracy. It can also process long videos, making it handy for workflows that involve video frames or multi-page documents. Since it’s hosted on Hugging Face, it’s also easy to plug into Python pipelines.

5. H2OVL-Mississippi

Link: https://h2o.ai/platform/mississippi/
H2OVL-Mississippi, from H2O.ai, offers two compact vision-language models: 0.8B and 2B). The smaller 0.8B model is focused purely on text recognition and actually beats much larger models like InternVL2-26B on OCRBench for that specific task. The 2B model is more general-purpose, handling tasks like image captioning and visual question answering alongside OCR. Trained on 37 million image-text pairs, these models are optimized for on-device deployment, making them ideal for privacy-focused applications in enterprise settings.

6. Florence-2

7. Surya

Link: https://github.com/VikParuchuri/surya
Surya is a Python-based OCR toolkit that supports line-level text detection and recognition in over 90+ languages. It outperforms Tesseract in inference time and accuracy, with over 5,000 GitHub stars reflecting its popularity. It outputs character/word/line bounding boxes and excels in layout analysis, identifying elements like tables, images, and headers. This makes Surya a perfect choice for structured document processing.

8. Moondream2

Link: https://huggingface.co/vikhyatk/moondream2
Moondream2 is a compact, open-source vision-language model with under 2 billion parameters, designed for resource-constrained devices . It offers fast, real-time document scanning capabilities. It recently improved its OCRBench score to 61.2, which shows better performance in reading printed text. While it’s not great with handwriting, it works well for forms, tables, and other structured documents. Its 1GB size and ability to run on edge devices make it a practical choice for applications like real-time document scanning on mobile devices.

9. GOT-OCR2

Link: https://github.com/Ucas-HaoranWei/GOT-OCR2.0
GOT-OCR2, or General OCR Theory - OCR 2.0, is a unified, end-to-end model with 580 million parameters, designed to handle diverse OCR tasks, including plain text, tables, charts, and equations. It supports scene and document-style images, generating plain or formatted outputs (e.g., markdown, LaTeX) via simple prompts. GOT-OCR2 pushes the boundaries of OCR-2.0 by processing artificial optical signals like sheet music and molecular formulas, making it ideal for specialized applications in academia and industry.

10. docTR

Link: https://www.mindee.com/platform/doctr
docTR, developed by Mindee, is an open-source OCR library optimized for document understanding. It uses a two-stage approach (text detection and recognition) with pre-trained models like db_resnet50 and crnn_vgg16_bn, achieving high performance on datasets like FUNSD and CORD. Its user-friendly interface requires just three lines of code to extract text, and it supports both CPU and GPU inference. docTR is ideal for developers needing quick, accurate document processing for receipts and forms.

Wrapping Up

That wraps up the list of top OCR models to watch in 2025. While there are many other great models available, this list focuses on the best across different categories—language models, Python frameworks, cloud-based services, and lightweight options for resource-constrained devices. If there’s an OCR model you think should be included, feel free to share its name in the comment section below.

Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook "Maximizing Productivity with ChatGPT". As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She's also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.