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OCR models have gone through a long way. What was once snail-paced, shiny and barely useful tools have now turned into quick, exact systems that can read almost anything, from handwritten notes to multilingual PDF files. If you work with unstructured data, building automation or configuring everything that includes scanned documents or images with text, OCR is crucial.
You probably already know ordinary names, such as Tesseract, Easyocr, Paddleocr and maybe Google Vision. They have been for some time and did the work. But to be forthright, 2025 is different. Today’s OCR models are faster, more exact and capable of serving much more elaborate tasks, such as real -time recognition of the text of the stage, multilingual parsing and classification of enormous -scale documents.
I conducted research to bring a list of the best OCR models that you should apply in 2025. This list comes from Github, research documents and industry updates covering Open Source and commercial options. Let’s start.
1. minicpm-o
To combine: https://huggingface.co/openbmb/minicpm-o-2_6
Minicpm-O was one of the most impressive OCR models I have recently met. This lithe model (only 8b parameters) developed by OpenBMB can process images with any shape coefficient of up to 1.8 million pixels. This makes it ideal for scanning of high resolution documents. It is currently at the top Ocrbench leaders board with version 2.6. It is 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 is the effective apply of tokens (640 tokens for the 1.8 MP image), thanks to which it is not only quick, but also ideal for mobile implementation or edges.
2. A trainee
3. Mistral OCR
To combine: https://mistral.ai/news/mistral-ocr
Mistral OCR started at the beginning of 2025 and quickly became one of the most reliable tools to understand the documents. Built by Mistral AI, API works well with submitted documents, such as PDF, scanned images, tables and equations. Carefully separates the text and visualizations, making it useful for RAG. . It supports many languages and results, which causes formats such as Markdown, which aid keep the structure tidy. Prices start from USD 1,000 per 1000 pages, and the batch processing offers better value. The last Mistral-ACR-2505 updated updated has improved the performance of handwriting and tables, which makes it a robust choice for anyone who works with detailed or mixed documents.
4. QWEN2-VL
5. H2ovl-Mississippi
To combine: https://h2o.ai/platform/mississisippi/
H2ovl-Mississippi with H2O.Ai offers two compact models in the vision language: 0.8b and 2b). The smaller 0.8b model focuses only on recognizing the text and actually beats much larger models, such as Internvl2-26b on Ocrbench for this particular task. Model 2b is more general, supporting tasks such as image signature and answering the visual question next to OCR. These models, trained in the range of 37 million pairs of image text, are optimized for implementation on the device, which makes them ideal for applications focused on privacy in the company’s settings.
6. Florence-2
To combine: https://h2o.ai/platform/mississisippi/
H2ovl-Mississippi with H2O.Ai offers two compact models in the vision language: 0.8b and 2b). The smaller 0.8b model focuses only on recognizing the text and actually beats much larger models, such as Internvl2-26b on Ocrbench for this particular task. Model 2b is more general, supporting tasks such as image signature and answering the visual question next to OCR. These models, trained in the range of 37 million pairs of image text, are optimized for implementation on the device, which makes them ideal for applications focused on privacy in the company’s settings.
7. Surya
To combine: https://github.com/vikparuchuri/surya
Surya is a set of OCR tools based on Python, which supports the detection and recognition of text at the line level in over 90 languages. It ahead of Tesseract during and accuracy, and over 5,000 GitHub stars reflect its popularity. Displays fields limiting the sign/word/line in the system analysis, identifying elements such as tables, images and headers. This makes Surya a great choice for structured document processing.
8. moondream2
To combine: https://huggingface.co/vikhyatk/mondream2
Mondream2 is a compact model in the language of open open vision from below 2 billion parameters, designed for devices confined by resources. It offers quick options for scanning documents in real time. Recently improved his Ocrbench result to 61.2, which shows better performance in reading text. Although this is not great in handwriting, it works well for forms, tables and other structured documents. Its size 1 GB and the ability to operate on EDGE devices make it a practical selection of applications such as real -time scanning on mobile devices.
9. Got-Cor2
To combine: https://github.com/ucas-haoranwei/got-ocr2.0
Got-Cor2, i.e. the general theory of OCR-OCR 2.0, is a unified, comprehensive model with 580 million parameters, designed to support various OCR tasks, including ordinary text, tables, charts and equations. It supports scenes and documents images, generating elementary or formatted outputs (e.g. Markdown, Latex) with elementary hints. Got-Cor2 shifts the boundaries of OCR-2.0 by processing artificial optical signals, such as music sheet and molecular formulas, which makes it ideal for specialized applications in the academic environment and industry.
10.
To combine: https://www.minee.com/platform/doctr
DocTR, developed by Minde, is a OCR library with optimized in the scope to understand documents. It uses a two -stage approach (detection and recognition of text) with previously trained models, such as DB_resnet50 and Crnn_VGG16_BN, achieving high performance on data sets such as FUNSD and CORD. His user -friendly interface requires only three code lines to separate the text and supports the application for a processor and a GPU. Doctr is ideal for programmers who need quick, thorough processing of documents for bills and forms.
Wrapping
This ends with a list of the best OCR models to watch in 2025, although many other great models are available, this list focuses on the best in different categories-models of the language, part of Python, cloud-based services and lithe options for equipment limiting resources. If there is an OCR model that you think should be taken into account, you can share your name in the comments section below.
Canwal Mehreen Kanwal is a machine learning engineer and a technical writer with a deep passion for data learning and AI intersection with medicine. He is the co -author of the ebook “maximizing performance from chatgpt”. As a Google 2022 generation scholar for APAC, it tells diversity and academic perfection. It is also recognized as a variety of terradate at Tech Scholar, Mitacs Globalink Research Scholar and Harvard Wecode Scholar. Kanwalwal is a scorching supporter of changes, after establishing FemCodes to strengthen women in the STEM fields.