DIMI-Arabic-OCR-V2

DIMI Arabic OCR v2

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Accurate Arabic OCR model V2 for extracting printed Arabic text from images

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Model Description

DIMI Arabic OCR v2 is a specialized Arabic Optical Character Recognition model fine-tuned on Qwen2.5-VL-7B-Instruct using LoRA adapters. This is the second iteration, building upon v1 with improved diacritics handling and enhanced accuracy across diverse Arabic text scenarios.

• Developed by: Ahmed Zaky
• Base Model: AhmedZaky1/DIMI-Arabic-OCR (v1)
• Original Base: Qwen/Qwen2.5-VL-7B-Instruct
• Model Type: Vision-Language Model (VLM) for Arabic OCR
• Language: Arabic (ar)
• License: Apache 2.0
• Fine-tuning Method: LoRA (Low-Rank Adaptation) with 4-bit quantization

Key Improvements Over v1

✅ 30% reduction in WER on diacritics-heavy text
✅ Enhanced training dataset with balanced diacritics representation
✅ Improved generalization across news articles and formal documents
✅ Better preservation of text formatting and structure

📊 Performance Metrics

Test Set Results (500 samples from 2,600)

Validation Set Results (100 samples)

Comparison with v1

Improvements:

• WER reduced by ~24.5% (0.404 → 0.3049)
• CER reduced by ~50.5% (0.226 → 0.1119)

🎯 Intended Use

Direct Use

This model is designed for extracting Arabic text from images, including:

• 📰 News articles and printed documents
• 📝 Formal Arabic text with diacritics (تشكيل)
• 🔢 Mixed Arabic text and numbers
• 📄 Scanned documents and screenshots

Example Use Case

from unsloth import FastVisionModel
from PIL import Image
import torch

# Load model
model, tokenizer = FastVisionModel.from_pretrained(
    "AhmedZaky1/DIMI-Arabic-OCR-v2",
    load_in_4bit=True,
    device_map="auto"
)
FastVisionModel.for_inference(model)

# Load image
image = Image.open("arabic_document.jpg")

# Prepare prompt
instruction = "استخرج النص العربي والأرقام الموجودة في هذه الصورة بدقة عالية."

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": instruction},
        ],
    }
]

# Apply chat template
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

# Tokenize
inputs = tokenizer(
    text=[text],
    images=[image],
    padding=True,
    return_tensors="pt",
    truncation=False
).to("cuda")

# Generate
with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=2048,
        do_sample=False
    )

# Decode
generated_ids = [
    out[len(inp):] for inp, out in zip(inputs.input_ids, outputs)
]
prediction = tokenizer.batch_decode(
    generated_ids, 
    skip_special_tokens=True
)[0]

print(prediction)

🧾 Training Data

Fine-tuned on 11,000 Arabic text images combining:

1. oddadmix/qari-0.2.2-news-dataset-large
2. oddadmix/qari-0.2.2-diacritics-dataset-large

The dataset covers modern standard Arabic with and without diacritics.

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📚 Citation

If you use this model, please cite:

@misc{dimi-arabic-ocr-2025,
  author = {Ahmed Zaky},
  title = {DIMI-Arabic-OCR: Fine-tuned Qwen2.5-VL for Arabic Text Recognition},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/AhmedZaky1/DIMI-Arabic-OCR}}
}

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🔗 Related Projects

• DIMI Models Series — Arabic Vision & Language Models

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Built with ❤️ by Ahmed Zaky

Advancing Arabic NLP through state-of-the-art embedding models

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