Picture by Writer | Canva
OCR fashions have come a good distance. What was once gradual, glitchy, and barely usable instruments have now changed into quick, correct techniques that may learn absolutely anything from handwritten notes to multi-language PDFs. In case you’re working with unstructured information, constructing automations, or organising something that includes scanned paperwork or pictures with textual content, OCR is vital.
You’re most likely already acquainted with the same old names like Tesseract, EasyOCR, PaddleOCR, and possibly Google Imaginative and prescient. They’ve been round for some time and have accomplished the job. However truthfully, 2025 feels completely different. Right now’s OCR fashions are sooner, extra correct, and able to dealing with far more advanced duties like real-time scene textual content recognition, multilingual parsing, and large-scale doc classification.
I’ve accomplished the analysis to carry you an inventory of the most effective OCR fashions you need to be utilizing in 2025. This checklist is sourced from GitHub, analysis papers, and trade updates masking each open-source and business choices. So, let’s get began.
1. MiniCPM-o
Hyperlink: https://huggingface.co/openbmb/MiniCPM-o-2_6
MiniCPM-o has been probably the most spectacular OCR fashions I’ve come throughout not too long ago. Developed by OpenBMB, this light-weight mannequin (solely 8B parameters) can course of pictures with any side ratio as much as 1.8 million pixels. This makes it supreme for high-resolution doc scanning. It presently tops the OCRBench leaderboard with model 2.6. That’s larger than a number of the greatest names within the recreation, together with GPT-4o, GPT-4V, and Gemini 1.5 Professional. It additionally has help for over 30 languages. One other factor I like about it’s the environment friendly token utilization (640 tokens for a 1.8MP picture), making it not solely quick but additionally good for cell or edge deployments.
2. InternVL
Hyperlink: https://github.com/OpenGVLab/InternVL
InternVL is a strong open-source OCR and vision-language mannequin developed by OpenGVLab. It is a robust different to closed fashions like GPT-4V, particularly for duties like doc understanding, scene textual content recognition, and multimodal evaluation. InternVL 2.0 can deal with high-resolution pictures (as much as 4K) by breaking them into smaller 448×448 tiles, making it environment friendly for big paperwork. It additionally acquired an 8k context window, which suggests it may well deal with longer and extra advanced paperwork with ease. InternVL 3 is the most recent within the sequence and takes issues even additional. It’s not nearly OCR anymore—this model expands into device use, 3D imaginative and prescient, GUI brokers, and even industrial picture evaluation.
3. Mistral OCR
Hyperlink: https://mistral.ai/information/mistral-ocr
Mistral OCR launched in early 2025 and has shortly turn out to be probably the most dependable instruments for doc understanding. Constructed by Mistral AI, the API works nicely with advanced paperwork like PDFs, scanned pictures, tables, and equations. It precisely extracts textual content and visuals collectively, making it helpful for RAG. . It helps a number of languages and outputs leads to codecs like markdown, which helps maintain the construction clear. Pricing begins at $1 per 1,000 pages, with batch processing providing higher worth. The latest mistral-ocr-2505 replace improved its efficiency on handwriting and tables, making it a robust selection for anybody working with detailed or mixed-format paperwork.
4. Qwen2-VL
Hyperlink: https://github.com/QwenLM
Qwen2-VL, a part of Alibaba’s Qwen sequence, is a strong open-source vision-language mannequin that I’ve discovered extremely helpful for OCR duties in 2025. It’s accessible in a number of sizes, together with 2B, 7B, and 72B parameters, and helps over 90 languages. The two.5-VL model performs rather well on benchmarks like DocVQA and MathVista, and even comes near GPT-4o in accuracy. It will probably additionally course of lengthy movies, making it useful for workflows that contain video frames or multi-page paperwork. Because it’s hosted on Hugging Face, it’s additionally simple to plug into Python pipelines.
5. H2OVL-Mississippi
Hyperlink: https://h2o.ai/platform/mississippi/
H2OVL-Mississippi, from H2O.ai, provides two compact vision-language fashions: 0.8B and 2B). The smaller 0.8B mannequin is targeted purely on textual content recognition and truly beats a lot bigger fashions like InternVL2-26B on OCRBench for that particular activity. The 2B mannequin is extra general-purpose, dealing with duties like picture captioning and visible query answering alongside OCR. Educated on 37 million image-text pairs, these fashions are optimized for on-device deployment, making them supreme for privacy-focused functions in enterprise settings.
6. Florence-2
Hyperlink: https://h2o.ai/platform/mississippi/
H2OVL-Mississippi, from H2O.ai, provides two compact vision-language fashions: 0.8B and 2B). The smaller 0.8B mannequin is targeted purely on textual content recognition and truly beats a lot bigger fashions like InternVL2-26B on OCRBench for that particular activity. The 2B mannequin is extra general-purpose, dealing with duties like picture captioning and visible query answering alongside OCR. Educated on 37 million image-text pairs, these fashions are optimized for on-device deployment, making them supreme for privacy-focused functions in enterprise settings.
7. Surya
Hyperlink: https://github.com/VikParuchuri/surya
Surya is a Python-based OCR toolkit that helps line-level textual content detection and recognition in over 90+ languages. It outperforms Tesseract in inference time and accuracy, with over 5,000 GitHub stars reflecting its recognition. It outputs character/phrase/line bounding containers and excels in structure evaluation, figuring out components like tables, pictures, and headers. This makes Surya an ideal selection for structured doc processing.
8. Moondream2
Hyperlink: https://huggingface.co/vikhyatk/moondream2
Moondream2 is a compact, open-source vision-language mannequin with underneath 2 billion parameters, designed for resource-constrained gadgets . It provides quick, real-time doc scanning capabilities. It not too long ago improved its OCRBench rating to 61.2, which exhibits higher efficiency in studying printed textual content. Whereas it’s not nice with handwriting, it really works nicely for varieties, tables, and different structured paperwork. Its 1GB measurement and skill to run on edge gadgets make it a sensible selection for functions like real-time doc scanning on cell gadgets.
9. GOT-OCR2
Hyperlink: https://github.com/Ucas-HaoranWei/GOT-OCR2.0
GOT-OCR2, or Normal OCR Principle – OCR 2.0, is a unified, end-to-end mannequin with 580 million parameters, designed to deal with various OCR duties, together with plain textual content, tables, charts, and equations. It helps scene and document-style pictures, producing plain or formatted outputs (e.g., markdown, LaTeX) by way of easy prompts. GOT-OCR2 pushes the boundaries of OCR-2.0 by processing synthetic optical alerts like sheet music and molecular formulation, making it supreme for specialised functions in academia and trade.
10. docTR
Hyperlink: https://www.mindee.com/platform/doctr
docTR, developed by Mindee, is an open-source OCR library optimized for doc understanding. It makes use of a two-stage method (textual content detection and recognition) with pre-trained fashions like db_resnet50 and crnn_vgg16_bn, attaining excessive efficiency on datasets like FUNSD and CORD. Its user-friendly interface requires simply three strains of code to extract textual content, and it helps each CPU and GPU inference. docTR is good for builders needing fast, correct doc processing for receipts and varieties.
Wrapping Up
That wraps up the checklist of high OCR fashions to look at in 2025. Whereas there are lots of different nice fashions accessible, this checklist focuses on the most effective throughout completely different classes—language fashions, Python frameworks, cloud-based companies, and light-weight choices for resource-constrained gadgets. If there’s an OCR mannequin you assume needs to be included, be at liberty to share its identify within the remark part under.
Kanwal Mehreen Kanwal is a machine studying engineer and a technical author with a profound ardour for information science and the intersection of AI with medication. She co-authored the book “Maximizing Productiveness with ChatGPT”. As a Google Era Scholar 2022 for APAC, she champions range and educational excellence. She’s additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.
Picture by Writer | Canva
OCR fashions have come a good distance. What was once gradual, glitchy, and barely usable instruments have now changed into quick, correct techniques that may learn absolutely anything from handwritten notes to multi-language PDFs. In case you’re working with unstructured information, constructing automations, or organising something that includes scanned paperwork or pictures with textual content, OCR is vital.
You’re most likely already acquainted with the same old names like Tesseract, EasyOCR, PaddleOCR, and possibly Google Imaginative and prescient. They’ve been round for some time and have accomplished the job. However truthfully, 2025 feels completely different. Right now’s OCR fashions are sooner, extra correct, and able to dealing with far more advanced duties like real-time scene textual content recognition, multilingual parsing, and large-scale doc classification.
I’ve accomplished the analysis to carry you an inventory of the most effective OCR fashions you need to be utilizing in 2025. This checklist is sourced from GitHub, analysis papers, and trade updates masking each open-source and business choices. So, let’s get began.
1. MiniCPM-o
Hyperlink: https://huggingface.co/openbmb/MiniCPM-o-2_6
MiniCPM-o has been probably the most spectacular OCR fashions I’ve come throughout not too long ago. Developed by OpenBMB, this light-weight mannequin (solely 8B parameters) can course of pictures with any side ratio as much as 1.8 million pixels. This makes it supreme for high-resolution doc scanning. It presently tops the OCRBench leaderboard with model 2.6. That’s larger than a number of the greatest names within the recreation, together with GPT-4o, GPT-4V, and Gemini 1.5 Professional. It additionally has help for over 30 languages. One other factor I like about it’s the environment friendly token utilization (640 tokens for a 1.8MP picture), making it not solely quick but additionally good for cell or edge deployments.
2. InternVL
Hyperlink: https://github.com/OpenGVLab/InternVL
InternVL is a strong open-source OCR and vision-language mannequin developed by OpenGVLab. It is a robust different to closed fashions like GPT-4V, particularly for duties like doc understanding, scene textual content recognition, and multimodal evaluation. InternVL 2.0 can deal with high-resolution pictures (as much as 4K) by breaking them into smaller 448×448 tiles, making it environment friendly for big paperwork. It additionally acquired an 8k context window, which suggests it may well deal with longer and extra advanced paperwork with ease. InternVL 3 is the most recent within the sequence and takes issues even additional. It’s not nearly OCR anymore—this model expands into device use, 3D imaginative and prescient, GUI brokers, and even industrial picture evaluation.
3. Mistral OCR
Hyperlink: https://mistral.ai/information/mistral-ocr
Mistral OCR launched in early 2025 and has shortly turn out to be probably the most dependable instruments for doc understanding. Constructed by Mistral AI, the API works nicely with advanced paperwork like PDFs, scanned pictures, tables, and equations. It precisely extracts textual content and visuals collectively, making it helpful for RAG. . It helps a number of languages and outputs leads to codecs like markdown, which helps maintain the construction clear. Pricing begins at $1 per 1,000 pages, with batch processing providing higher worth. The latest mistral-ocr-2505 replace improved its efficiency on handwriting and tables, making it a robust selection for anybody working with detailed or mixed-format paperwork.
4. Qwen2-VL
Hyperlink: https://github.com/QwenLM
Qwen2-VL, a part of Alibaba’s Qwen sequence, is a strong open-source vision-language mannequin that I’ve discovered extremely helpful for OCR duties in 2025. It’s accessible in a number of sizes, together with 2B, 7B, and 72B parameters, and helps over 90 languages. The two.5-VL model performs rather well on benchmarks like DocVQA and MathVista, and even comes near GPT-4o in accuracy. It will probably additionally course of lengthy movies, making it useful for workflows that contain video frames or multi-page paperwork. Because it’s hosted on Hugging Face, it’s additionally simple to plug into Python pipelines.
5. H2OVL-Mississippi
Hyperlink: https://h2o.ai/platform/mississippi/
H2OVL-Mississippi, from H2O.ai, provides two compact vision-language fashions: 0.8B and 2B). The smaller 0.8B mannequin is targeted purely on textual content recognition and truly beats a lot bigger fashions like InternVL2-26B on OCRBench for that particular activity. The 2B mannequin is extra general-purpose, dealing with duties like picture captioning and visible query answering alongside OCR. Educated on 37 million image-text pairs, these fashions are optimized for on-device deployment, making them supreme for privacy-focused functions in enterprise settings.
6. Florence-2
Hyperlink: https://h2o.ai/platform/mississippi/
H2OVL-Mississippi, from H2O.ai, provides two compact vision-language fashions: 0.8B and 2B). The smaller 0.8B mannequin is targeted purely on textual content recognition and truly beats a lot bigger fashions like InternVL2-26B on OCRBench for that particular activity. The 2B mannequin is extra general-purpose, dealing with duties like picture captioning and visible query answering alongside OCR. Educated on 37 million image-text pairs, these fashions are optimized for on-device deployment, making them supreme for privacy-focused functions in enterprise settings.
7. Surya
Hyperlink: https://github.com/VikParuchuri/surya
Surya is a Python-based OCR toolkit that helps line-level textual content detection and recognition in over 90+ languages. It outperforms Tesseract in inference time and accuracy, with over 5,000 GitHub stars reflecting its recognition. It outputs character/phrase/line bounding containers and excels in structure evaluation, figuring out components like tables, pictures, and headers. This makes Surya an ideal selection for structured doc processing.
8. Moondream2
Hyperlink: https://huggingface.co/vikhyatk/moondream2
Moondream2 is a compact, open-source vision-language mannequin with underneath 2 billion parameters, designed for resource-constrained gadgets . It provides quick, real-time doc scanning capabilities. It not too long ago improved its OCRBench rating to 61.2, which exhibits higher efficiency in studying printed textual content. Whereas it’s not nice with handwriting, it really works nicely for varieties, tables, and different structured paperwork. Its 1GB measurement and skill to run on edge gadgets make it a sensible selection for functions like real-time doc scanning on cell gadgets.
9. GOT-OCR2
Hyperlink: https://github.com/Ucas-HaoranWei/GOT-OCR2.0
GOT-OCR2, or Normal OCR Principle – OCR 2.0, is a unified, end-to-end mannequin with 580 million parameters, designed to deal with various OCR duties, together with plain textual content, tables, charts, and equations. It helps scene and document-style pictures, producing plain or formatted outputs (e.g., markdown, LaTeX) by way of easy prompts. GOT-OCR2 pushes the boundaries of OCR-2.0 by processing synthetic optical alerts like sheet music and molecular formulation, making it supreme for specialised functions in academia and trade.
10. docTR
Hyperlink: https://www.mindee.com/platform/doctr
docTR, developed by Mindee, is an open-source OCR library optimized for doc understanding. It makes use of a two-stage method (textual content detection and recognition) with pre-trained fashions like db_resnet50 and crnn_vgg16_bn, attaining excessive efficiency on datasets like FUNSD and CORD. Its user-friendly interface requires simply three strains of code to extract textual content, and it helps each CPU and GPU inference. docTR is good for builders needing fast, correct doc processing for receipts and varieties.
Wrapping Up
That wraps up the checklist of high OCR fashions to look at in 2025. Whereas there are lots of different nice fashions accessible, this checklist focuses on the most effective throughout completely different classes—language fashions, Python frameworks, cloud-based companies, and light-weight choices for resource-constrained gadgets. If there’s an OCR mannequin you assume needs to be included, be at liberty to share its identify within the remark part under.
Kanwal Mehreen Kanwal is a machine studying engineer and a technical author with a profound ardour for information science and the intersection of AI with medication. She co-authored the book “Maximizing Productiveness with ChatGPT”. As a Google Era Scholar 2022 for APAC, she champions range and educational excellence. She’s additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.
