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--- |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- qwen3_vl |
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- trl |
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- sft |
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- chemistry |
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- code |
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- climate |
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- art |
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- biology |
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- finance |
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- legal |
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- music |
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- medical |
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- agent |
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license: apache-2.0 |
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language: |
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- en |
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- ab |
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- aa |
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- ae |
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- af |
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- ak |
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- am |
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- an |
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- ar |
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- as |
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- av |
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- ay |
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- az |
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- ba |
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- be |
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- bg |
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- bh |
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- bi |
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- bm |
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- bn |
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- bo |
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- br |
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- bs |
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- ca |
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- ce |
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- ch |
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- co |
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- cr |
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- cs |
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- cu |
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- cv |
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- cy |
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- da |
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- de |
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- dv |
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- dz |
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- ee |
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- el |
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- eo |
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- es |
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- et |
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- eu |
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- fa |
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- ff |
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- fi |
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- fj |
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- fo |
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- fr |
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- fy |
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- ga |
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- gd |
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- gl |
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- gn |
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- gv |
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- ha |
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- he |
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- hi |
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- ho |
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- gu |
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- hr |
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- ht |
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- hu |
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- hz |
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- hy |
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- id |
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- ia |
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- ig |
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- ie |
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- ik |
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- ii |
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- is |
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- io |
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- iu |
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- it |
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- jv |
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- ja |
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- kg |
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- ka |
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- kj |
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- ki |
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- kl |
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- kk |
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- kn |
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- km |
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- kr |
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- ko |
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- ku |
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- ks |
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- kw |
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- kv |
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- la |
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- ky |
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- lg |
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- lb |
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- ln |
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- li |
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- lt |
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- lo |
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- lv |
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- lu |
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- mg |
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- mi |
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- mh |
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- ml |
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- mk |
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- mr |
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- mn |
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- mt |
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- ms |
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- na |
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- my |
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- nd |
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- nb |
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- ng |
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- nl |
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- ne |
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- 'no' |
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- nn |
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- nv |
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- nr |
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- oc |
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- oj |
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- om |
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- ny |
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- os |
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- or |
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- pa |
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- pi |
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- pl |
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- ps |
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- pt |
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- rm |
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- rn |
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- qu |
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- ro |
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- ru |
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- sn |
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- rw |
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- so |
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- sa |
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- sc |
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- sd |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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--- |
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<img src='bannerocr.png'> |
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# 🖼️ Next OCR 8B |
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### *Compact OCR AI — Accurate, Fast, Multilingual, Math-Optimized* |
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[](https://opensource.org/licenses/MIT) |
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[]() |
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[](https://huggingface.co/Lamapi/next-ocr) |
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--- |
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## 📖 Overview |
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**Next OCR 8B** is an **8-billion parameter model** optimized for **optical character recognition (OCR) tasks** with **mathematical and tabular content understanding**. |
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Supports **multilingual OCR** (Turkish, English, German, Spanish, French, Chinese, Japanese, Korean, Russian...) with high accuracy, including structured documents like tables, forms, and formulas. |
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--- |
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## ⚡ Highlights |
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* 🖼️ Accurate text extraction, including math and tables |
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* 🌍 Multilingual support (30+ languages) |
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* ⚡ Lightweight and efficient |
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* 💬 Instruction-tuned for document understanding and analysis |
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--- |
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## 📊 Benchmark & Comparison |
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 |
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--- |
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| Model | OCR-Bench Accuracy (%) | Multilingual Accuracy (%) | Layout / Table Understanding (%) | |
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| ------------------------------- | ------------------------ | ------------------------- | -------------------------------- | |
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| **Next OCR** | **99.0** | **96.8** | **95.3** | |
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| PaddleOCR | 95.2 | 93.9 | 95.3 | |
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| Deepseek OCR | 90.6 | 87.4 | 86.1 | |
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| Tesseract | 92.0 | 88.4 | 72.0 | |
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| EasyOCR | 90.4 | 84.7 | 78.9 | |
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| Google Cloud Vision / DocAI | 98.7 | 95.5 | 93.6 | |
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| Amazon Textract | 94.7 | 86.2 | 86.1 | |
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| Azure Document Intelligence | 95.1 | 93.6 | 91.4 | |
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--- |
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| Model | Handwriting (%) | Scene Text (%) | Complex Tables (%) | |
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| --------------------------- | --------------- | -------------- | ------------------ | |
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| **Next OCR** | 92 | 96 | 91 | |
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| PaddleOCR | 88 | 92 | 90 | |
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| Deepseek OCR | 80 | 85 | 83 | |
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| Tesseract | 75 | 88 | 70 | |
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| EasyOCR | 78 | 86 | 75 | |
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| Google Cloud Vision / DocAI | 90 | 95 | 92 | |
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| Amazon Textract | 85 | 90 | 88 | |
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| Azure Document Intelligence | 87 | 91 | 89 | |
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--- |
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## 🚀 Installation & Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForVision2Seq |
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import torch |
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model_id = "Lamapi/next-ocr" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype=torch.float16) |
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img = Image.open("image.jpg") |
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# ATTENTION: The content list must include both an image and text. |
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messages = [ |
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{"role": "system", "content": "You are Next-OCR, an helpful AI assistant trained by Lamapi."}, |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "image", "image": img}, |
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{"type": "text", "text": "Read the text in this image and summarize it."} |
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] |
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} |
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] |
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# Apply the chat template correctly |
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prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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inputs = processor(text=prompt, images=[img], return_tensors="pt").to(model.device) |
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with torch.no_grad(): |
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generated = model.generate(**inputs, max_new_tokens=256) |
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print(processor.decode(generated[0], skip_special_tokens=True)) |
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``` |
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--- |
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## 🧩 Key Features |
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| Feature | Description | |
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| -------------------------- | --------------------------------------------------------------- | |
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| 🖼️ High-Accuracy OCR | Extracts text from images, documents, and screenshots reliably. | |
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| 🇹🇷 Multilingual Support | Works with 30+ languages including Turkish. | |
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| ⚡ Lightweight & Efficient | Optimized for resource-constrained environments. | |
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| 📄 Layout & Math Awareness | Handles tables, forms, and mathematical formulas. | |
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| 🏢 Reliable Outputs | Suitable for enterprise document workflows. | |
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--- |
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## 📐 Model Specifications |
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| Specification | Details | |
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| ----------------- | --------------------------------------------------------- | |
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| **Base Model** | Qwen 3 | |
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| **Parameters** | 8 Billion | |
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| **Architecture** | Vision + Transformer (OCR LLM) | |
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| **Modalities** | Image-to-text | |
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| **Fine-Tuning** | OCR datasets with multilingual and math/tabular content | |
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| **Optimizations** | Quantization-ready, FP16 support | |
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| **Primary Focus** | Text extraction, document understanding, mathematical OCR | |
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--- |
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## 🎯 Ideal Use Cases |
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* Document digitization |
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* Invoice & receipt processing |
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* Multilingual OCR pipelines |
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* Tables, forms, and formulas extraction |
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* Enterprise document management |
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--- |
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## 📄 License |
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MIT License — free for commercial & non-commercial use. |
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--- |
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## 📞 Contact & Support |
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* 📧 Email: [lamapicontact@gmail.com](mailto:lamapicontact@gmail.com) |
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* 🤗 HuggingFace: [Lamapi](https://huggingface.co/Lamapi) |
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--- |
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> **Next OCR** — Compact *OCR + math-capable* AI, blending **accuracy**, **speed**, and **multilingual document intelligence**. |
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[](https://huggingface.co/Lamapi) |
