🖼️ 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