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OCR models have made a long journey. Which are slow, glitchy and barely usable tools are now turned into a sharp, accurate system that can read anything from handwritten notes to multi -language PDF. If you are working with non -imposed data, making automation, or setting anything that includes photos with scanned documents or texts, the OCR is key.
You will probably be familiar with the usual names like Tasctrum, Easy Okar, Palesker, and Perhaps Google Vision. They have been for a while and have worked. But honestly, 2025 feels different. Today’s OCR models are able to handle fast, more accurate and more complex tasks such as real -time scene text identification, multi -linguistic analysis, and massive documentation.
I have researched you to list the best OCR models using you in 2025. This list is obtained from the Gut Hub, Research Articles, and the updates of both open sources and commercial options. So, let’s start.
1. MINICPM-O
Link: https://hugingface.co/openbmb/minicpm-o-2_6
MINICPM-O I’ve been one of the most impressive OCR models recently. Developed by Open BMB, this lightweight model (only 8B parameters) can take pictures with any aspect ratio up to 1.8 million pixels. This is ideal for scanning high resolution document. It’s currently the top Ocrbench Leader Board With version 2.6. This is more than the biggest names of the game, including GPT -4O, GPT -4V, and Gemini 1.5 Pro. It also has the support of more than 30 languages. Another thing I love about is the use of effective token (640 tokens for 1.8MP image), which is not only faster but also best for mobile or edge deployment.
2. Interolly
Link: https://github.com/opengvlab/internvl
The Interviel is a powerful open source OCR and the vision language model developed by Opangola. It is a strong alternative to closed models like GPT-4V, especially for documents, identification of scene text, and multitudes for tasks. The Interviel 2.0 can handle high resolution images (up to 4K) into small 448×448 tiles, making it effective efficient of large documents. It also found an 8K context window, which means it can easily handle longer and more complex documents. The Internet VL is the latest in the series and takes things even more. It’s no longer about OCR – this version spreads to the use of tools, 3D vision, GUI agents, and even industrial image analysis.
3. Invalid OCR
Link: https://mistral.ai/news/Mistral-ocr
Mr. OCR launched in early 2025 and has become a reliable tool for understanding the documents. Made by Mr AI, API works well with complex documents such as PDF, scanned images, tables and equations. It accurately removes text and visuals together, which is useful for RAG. . It supports the results of multiple languages ​​and outpots, such as Mark Dowan formats, which help keep the structure clear. Pricing starts at $ 1 on 1,000 pages, in which batch processing offers a better price. The recent Invalid -ORC -2505 update improves its performance on handwriting and tables, which is a strong choice for everyone working with detailed or mixed format documents.
4. Qwen2-vl
Link: https://github.com/qwenlm
A part of Alibaba’s Kevin series is Kevin 2-VL, a powerful open source vision language model that I found incredibly useful for OCR works in 2025. It is available in several sizes, including 2B, 7B, and 72B parameters, and supports more than 90 languages. The 2.5-VL version performs really well on benchmarks like DOCVQA and math mark, and even comes closer to GPT-4O in accuracy. It can also process long videos, which makes it easy for workflose, including video frames or multi -page documents. Since it is hosted on the hugging face, it is also easy to plug on the pipelines.
5.
Link: https://h2o.ai/platform/mississippi/
H2OVL-MUSTSIissippi, H2O.AI, offers two compact vision language models: 0.8b and 2b). The small 0.8B model is fully focused on the identification of the text and defeats very large models like the Interville 2-26B on this particular work O OCRBENCH. The 2b model is a more common goal, which is to handle tasks such as OCR as well as image captioning and visual question. Trained at 37 million image text couple, this model is better to deploy the device to Optim, which is ideal for privacy applications in enterprise settings.
6. Florence -2
Link: https://h2o.ai/platform/mississippi/
H2OVL-MUSTSIissippi, H2O.AI, offers two compact vision language models: 0.8b and 2b). The small 0.8B model is fully focused on the identification of the text and defeats very large models like the Interville 2-26B on this particular work O OCRBENCH. The 2b model is a more common goal, which is to handle tasks such as OCR as well as image captioning and visual question. Trained at 37 million image text couple, this model is better to deploy the device to Optim, which is ideal for privacy applications in enterprise settings.
7. Suriya
Link: https://github.com/vikparuchuri/surya
Suria is a fisherman -based OCR toolkit that supports the detection and identity of line surface text in more than 90+ languages. It improves the testing in a better time and accuracy, in which more than 5000 gut hub stars reflect its popularity. It pushes the Character/Word/Line Bounding Boxes and Excels in the layout analysis, identifying elements such as tables, photos, and headers. This makes Surya a great choice for the structural document processing.
8.
Link: https://hugingface.co/vikhyatk/moondremram2
Mondayrium 2 is a compact, open source vision language model with 2 billion parameters, designed for resource -affected devices. It offers fast, real -time document scanning capabilities. It recently improved its Okar Bench scores to 61.2, which shows better performance in reading the printed text. Although it is not great with handwriting, it works well for form, tables and other structured documents. Its 1GB size and the ability to run on Edge Devices make it a practical choice for applications such as real -time document scanning on mobile devices.
9. Got-Cocr2
Link: https://github.com/ucas-haranwei/got-ocr2.0
GOT-COCR2, or a united, united model with 580 million parameters, GOT-Cocr2, or ordinary OCR Theory-OCR 2.0, which is designed to handle diverse OCR tasks, including simple text, tables, charts and equations. It supports the scene and documentary style images, producing simple or formated output (eg, Mark Down, latex) by simple gestures. GOT-CR2 Sheets advances the boundaries of OCR-2.0 by acting on artificial optical signals such as music and molecular formulas, which makes it ideal for special applications in academia and industry.
10. Documentation
Link: https://www.mindee.com/platform/DOCTR
The document, which is developed by Mandi, is an open source OCR library that improves you Optim for understanding of the document. It uses a two -stage approach (text detection and identity), with pre -trained models such as DB_resnet50 and Crnn_VGG16_bn, high performance on datasters such as Funsd and Bone. Its user -friendly interface requires only three lines of code to remove the text, and it supports both CPUs and GPUs. Documents and forms are ideal for Quick Quick Prompt, accurate documentation processing developers.
Wrap
This wraps the list of top top top top OCR models to view in 2025. While many other great models are available, this list has best performance in different categories. If you think an OCR model should be included, share its name without hesitation in the comments section below.
Kanwal seals Kanwal is a machine learning engineer and a technical writer who has a deep passion for data science and has AI intersection with medicine. He authored EBook with “Maximum Production Capacity with Chat GPT”. As a Google Generation Scholar 2022 for the APAC, the Champions Diversity and the Educational Virtue. He is also recognized as a tech scholar, Mitacs Global Research Scholar, and Harvard Vacod Scholar as a Taradata diversity. Kanwal is a passionate lawyer for change, who has laid the foundation of a Fame Code to empower women in stem fields.