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Lamapi / next-ocr
Model's Last Updated: November 15 2025
image-text-to-text
Introduction of next-ocr
Model Details of next-ocr
🖼️ Next OCR 8B
Compact OCR AI — Accurate, Fast, Multilingual, Math-Optimized
📖 Overview
Next OCR 8B is an 8-billion parameter model optimized for optical character recognition (OCR) tasks with mathematical and tabular content understanding .
Supports multilingual OCR (Turkish, English, German, Spanish, French, Chinese, Japanese, Korean, Russian...) with high accuracy, including structured documents like tables, forms, and formulas.
⚡ Highlights
- 🖼️ Accurate text extraction, including math and tables
- 🌍 Multilingual support (30+ languages)
- ⚡ Lightweight and efficient
- 💬 Instruction-tuned for document understanding and analysis
📊 Benchmark & Comparison
| Model | OCR-Bench Accuracy (%) | Multilingual Accuracy (%) | Layout / Table Understanding (%) |
|---|---|---|---|
| Next OCR | 99.0 | 96.8 | 95.3 |
| PaddleOCR | 95.2 | 93.9 | 95.3 |
| Deepseek OCR | 90.6 | 87.4 | 86.1 |
| Tesseract | 92.0 | 88.4 | 72.0 |
| EasyOCR | 90.4 | 84.7 | 78.9 |
| Google Cloud Vision / DocAI | 98.7 | 95.5 | 93.6 |
| Amazon Textract | 94.7 | 86.2 | 86.1 |
| Azure Document Intelligence | 95.1 | 93.6 | 91.4 |
| Model | Handwriting (%) | Scene Text (%) | Complex Tables (%) |
|---|---|---|---|
| Next OCR | 92 | 96 | 91 |
| PaddleOCR | 88 | 92 | 90 |
| Deepseek OCR | 80 | 85 | 83 |
| Tesseract | 75 | 88 | 70 |
| EasyOCR | 78 | 86 | 75 |
| Google Cloud Vision / DocAI | 90 | 95 | 92 |
| Amazon Textract | 85 | 90 | 88 |
| Azure Document Intelligence | 87 | 91 | 89 |
🚀 Installation & Usage
from transformers import AutoTokenizer, AutoModelForVision2Seq
import torch
model_id = "Lamapi/next-ocr"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype=torch.float16)
img = Image.open("image.jpg")
# ATTENTION: The content list must include both an image and text.
messages = [
{"role": "system", "content": "You are Next-OCR, an helpful AI assistant trained by Lamapi."},
{
"role": "user",
"content": [
{"type": "image", "image": img},
{"type": "text", "text": "Read the text in this image and summarize it."}
]
}
]
# Apply the chat template correctly
prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=prompt, images=[img], return_tensors="pt").to(model.device)
with torch.no_grad():
generated = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(generated[0], skip_special_tokens=True))
🧩 Key Features
| Feature | Description |
|---|---|
| 🖼️ High-Accuracy OCR | Extracts text from images, documents, and screenshots reliably. |
| 🇹🇷 Multilingual Support | Works with 30+ languages including Turkish. |
| ⚡ Lightweight & Efficient | Optimized for resource-constrained environments. |
| 📄 Layout & Math Awareness | Handles tables, forms, and mathematical formulas. |
| 🏢 Reliable Outputs | Suitable for enterprise document workflows. |
📐 Model Specifications
| Specification | Details |
|---|---|
| Base Model | Qwen 3 |
| Parameters | 8 Billion |
| Architecture | Vision + Transformer (OCR LLM) |
| Modalities | Image-to-text |
| Fine-Tuning | OCR datasets with multilingual and math/tabular content |
| Optimizations | Quantization-ready, FP16 support |
| Primary Focus | Text extraction, document understanding, mathematical OCR |
🎯 Ideal Use Cases
- Document digitization
- Invoice & receipt processing
- Multilingual OCR pipelines
- Tables, forms, and formulas extraction
- Enterprise document management
📄 License
MIT License — free for commercial & non-commercial use.
📞 Contact & Support
- 📧 Email: [email protected]
- 🤗 HuggingFace: Lamapi
Next OCR — Compact OCR + math-capable AI, blending accuracy , speed , and multilingual document intelligence .
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