AhmedZaky1
/

DIMI-Arabic-OCR-V2

@@ -1,210 +1,196 @@
 ---
-base_model: unsloth/qwen2.5-vl-7b-instruct-bnb-4bit
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/qwen2.5-vl-7b-instruct-bnb-4bit
 - lora
-- sft
-- transformers
-- trl
 - unsloth
 ---
-# Model Card for Model ID
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-## Model Details
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-### Framework versions
-- PEFT 0.17.1

 ---
+base_model: AhmedZaky1/DIMI-Arabic-OCR
 library_name: peft
+language:
+- ar
+pipeline_tag: image-text-to-text
 tags:
+- vision
+- ocr
+- arabic
+- qwen2.5-vl
 - lora
 - unsloth
+- trl
+- transformers
+license: apache-2.0
+datasets:
+- oddadmix/qari-0.2.2-news-dataset-large
+- oddadmix/qari-0.2.2-diacritics-dataset-large
+metrics:
+- wer
+- cer
 ---
+# DIMI Arabic OCR v2
+<div align="center">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/65fb3ac20cfe262da2bb0fcc/uOuEn0LNhSVEBbOLwfFUu.jpeg" width="300"/>
+*Accurate Arabic OCR model V2 for extracting printed Arabic text from images*
+</div>
+---
+## 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)
+| Metric | Score | Description |
+|--------|-------|-------------|
+| **WER** | 0.3049 | Word Error Rate (↓ lower is better) |
+| **CER** | 0.1119 | Character Error Rate (↓ lower is better) |
+| **Perfect Predictions** | 23% | Exact matches with ground truth |
+### Validation Set Results (100 samples)
+| Metric | Score |
+|--------|-------|
+| **WER** | 0.2315 |
+| **CER** | 0.0776 |
+### Comparison with v1
+| Model | Test WER | Test CER | Val WER | Val CER |
+|-------|----------|----------|---------|---------|
+| **v1** | 0.404 | 0.226 | 0.3308 | 0.1820 |
+| **v2** | **0.3049** ↓ | **0.1119** ↓ | **0.2315** | **0.0776** |
+**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
+```python
+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](https://huggingface.co/datasets/oddadmix/qari-0.2.2-news-dataset-large)
+2. [oddadmix/qari-0.2.2-diacritics-dataset-large](https://huggingface.co/datasets/oddadmix/qari-0.2.2-diacritics-dataset-large)
+The dataset covers modern standard Arabic with and without diacritics.
+---
+## 📚 Citation
+If you use this model, please cite:
+```bibtex
+@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}}
+}
+```
+---
+### 🔗 Related Projects
+- [DIMI Models Series](https://huggingface.co/AhmedZaky1) — Arabic Vision & Language Models
+---
+<div align="center">
+**Built with ❤️ by Ahmed Zaky**
+*Advancing Arabic NLP through state-of-the-art embedding models*
+</div>