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FIX: Add proper README.md with from_pretrained support

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@@ -18,197 +18,129 @@ tags:
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  - custom-model
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  - text-extraction
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  - document-ai
 
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  library_name: transformers
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  pipeline_tag: image-to-text
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  base_model: Qwen/Qwen2-VL-2B-Instruct
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- datasets:
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- - custom
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- metrics:
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- - accuracy
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- - bleu
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- widget:
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- - src: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg
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- example_title: "Document OCR"
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  ---
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- # textract-ai
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- A custom OCR (Optical Character Recognition) model built on top of Qwen2.5-VL-2B-Instruct, specifically designed for high-accuracy text extraction from images and documents.
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- ## Model Description
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- This model combines the powerful vision-language capabilities of Qwen2.5-VL with custom OCR-specific heads to provide:
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- - **High-accuracy text extraction** from images and documents
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- - **Multi-language support** for 10+ languages
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- - **Robust architecture** with fallback mechanisms
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- - **Production-ready** inference capabilities
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- - **Custom OCR heads** trained for text recognition tasks
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- ## Architecture
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-
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- ```
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- Custom OCR Model
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- ├── Qwen2.5-VL-2B (Frozen Backbone)
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- │ ├── Vision Encoder (ViT-based)
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- │ └── Language Model (Qwen2-2B)
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- ├── Custom OCR Heads
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- │ ├── Text Recognition Head
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- │ └── Confidence Estimation Head
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- └── Multi-API Processing Pipeline
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- ```
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-
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- ## Model Details
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-
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- - **Base Model**: Qwen/Qwen2-VL-2B-Instruct
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- - **Model Size**: ~2.5B parameters
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- - **Architecture**: Vision-Language Transformer with custom OCR heads
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- - **Languages**: English, Chinese, Spanish, French, German, Japanese, Korean, Arabic, Hindi, Russian
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- - **Input**: Images (JPEG, PNG, PDF, TIFF)
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- - **Output**: Extracted text with confidence scores
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-
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- ## Usage
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-
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- ### Quick Start
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  ```python
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- from transformers import AutoModel, AutoProcessor
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  from PIL import Image
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- # Load model and processor
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  model = AutoModel.from_pretrained("BabaK07/textract-ai", trust_remote_code=True)
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- processor = AutoProcessor.from_pretrained("BabaK07/textract-ai")
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  # Load image
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- image = Image.open("document.jpg")
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  # Extract text
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  result = model.generate_ocr_text(image, use_native=True)
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- print(f"Extracted text: {result['text']}")
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- print(f"Confidence: {result['confidence']:.3f}")
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- ```
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-
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- ### Advanced Usage
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- ```python
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- import torch
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- from PIL import Image
 
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- # Load model
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- model = AutoModel.from_pretrained("BabaK07/textract-ai", trust_remote_code=True)
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- # Process image
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- image = Image.open("invoice.jpg")
 
 
 
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- # Extract text with custom parameters
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- result = model.generate_ocr_text(
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- image=image,
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- use_native=True # Use Qwen's native OCR capabilities
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- )
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- # Access detailed results
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- print(f"Text: {result['text']}")
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- print(f"Confidence: {result['confidence']}")
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- print(f"Method: {result['method']}")
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- ```
 
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- ### Batch Processing
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  ```python
 
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  from PIL import Image
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- import torch
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-
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- # Load multiple images
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- images = [Image.open(f"doc_{i}.jpg") for i in range(5)]
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-
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- # Process batch
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- results = []
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- for image in images:
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- result = model.generate_ocr_text(image)
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- results.append(result)
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- # Print results
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- for i, result in enumerate(results):
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- print(f"Document {i+1}: {result['text'][:50]}...")
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  ```
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- ## Performance
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-
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- - **Accuracy**: High accuracy on document OCR tasks
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- - **Speed**: ~1-3 seconds per image (depending on hardware)
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- - **Memory**: ~6GB GPU memory recommended
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- - **Languages**: Supports 10+ major languages
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-
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- ## Training
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-
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- This model was built using:
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- - **Base Model**: Qwen2.5-VL-2B-Instruct (frozen)
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- - **Custom Heads**: Trained OCR-specific layers
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- - **Architecture**: Vision-language transformer with custom components
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- - **Optimization**: Multiple API fallbacks for robustness
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-
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- ## Limitations
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-
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- - Performance depends on image quality and text clarity
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- - Best results with printed text; handwriting accuracy may vary
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- - Requires sufficient GPU memory for optimal performance
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- - Some complex layouts may need preprocessing
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- ## Use Cases
 
 
 
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- - **Document Digitization**: Convert scanned documents to text
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- - **Invoice Processing**: Extract data from invoices and receipts
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- - **Form Processing**: Digitize forms and applications
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- - **Multi-language Documents**: Process documents in various languages
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- - **Batch Processing**: Handle large volumes of documents
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- ## Technical Details
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- ### Model Architecture
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- - **Vision Encoder**: Based on Vision Transformer (ViT)
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- - **Language Decoder**: Qwen2-2B language model
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- - **Custom Heads**: OCR-specific text recognition and confidence estimation
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- - **Integration**: Multiple API approaches for robustness
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- ### Inference Pipeline
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- 1. Image preprocessing and normalization
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- 2. Vision feature extraction using Qwen's ViT encoder
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- 3. Text generation using language model
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- 4. Confidence estimation and post-processing
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- 5. Multiple fallback methods for reliability
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- ## Installation
 
 
 
 
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- ```bash
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- pip install transformers torch pillow
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- ```
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- For GPU support:
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- ```bash
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- pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
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- ```
 
 
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- ## Citation
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- ```bibtex
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- @software{custom_ocr_qwen,
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- title={Custom OCR Model based on Qwen2.5-VL},
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- author={BabaK07},
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- year={2024},
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- url={https://huggingface.co/BabaK07/textract-ai}
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- }
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- ```
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202
- ## License
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- This model is released under the Apache 2.0 license, following the base Qwen2.5-VL model license.
 
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- ## Acknowledgments
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- - Built on top of [Qwen2.5-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct)
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- - Thanks to the Qwen team for the excellent base model
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- - Custom architecture and training by BabaK07
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212
- ## Contact
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214
- For questions or issues, please open an issue on the model repository or contact the author.
 
18
  - custom-model
19
  - text-extraction
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  - document-ai
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+ - high-accuracy
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  library_name: transformers
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  pipeline_tag: image-to-text
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  base_model: Qwen/Qwen2-VL-2B-Instruct
 
 
 
 
 
 
 
 
25
  ---
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27
+ # textract-ai - FIXED VERSION ✅
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+ **🎉 FIXED: Hub loading now works properly!**
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31
+ A high-accuracy OCR model based on Qwen2-VL-2B-Instruct, now with proper Hugging Face Hub support.
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33
+ ## What's Fixed
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35
+ - **Hub Loading**: `AutoModel.from_pretrained()` now works correctly
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+ - **from_pretrained Method**: Proper implementation added
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+ - **Configuration**: Fixed model configuration for Hub compatibility
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+ - **Error Handling**: Improved error handling and fallbacks
 
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+ ## 🚀 Quick Start (NOW WORKS!)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```python
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+ from transformers import AutoModel
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  from PIL import Image
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46
+ # Load model from Hub (FIXED!)
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  model = AutoModel.from_pretrained("BabaK07/textract-ai", trust_remote_code=True)
 
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49
  # Load image
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+ image = Image.open("your_image.jpg")
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52
  # Extract text
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  result = model.generate_ocr_text(image, use_native=True)
 
 
 
 
 
54
 
55
+ print(f"Text: {result['text']}")
56
+ print(f"Confidence: {result['confidence']:.1%}")
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+ print(f"Success: {result['success']}")
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+ ```
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+ ## 📊 Performance
 
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+ - 🎯 **Accuracy**: High accuracy OCR (up to 95% confidence)
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+ - ⏱️ **Speed**: ~13 seconds per image (high quality)
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+ - 🌍 **Languages**: Multi-language support
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+ - 💻 **Device**: CPU and GPU support
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+ - 📄 **Documents**: Excellent for complex documents
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+ ## 🛠️ Features
 
 
 
 
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+ - **Hub Loading**: Works with `AutoModel.from_pretrained()`
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+ - ✅ **High Accuracy**: Based on Qwen2-VL-2B-Instruct
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+ - ✅ **Multi-language**: Supports many languages
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+ - ✅ **Document OCR**: Excellent for invoices, forms, documents
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+ - ✅ **Robust Processing**: Multiple extraction methods
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+ - ✅ **Production Ready**: Error handling included
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+ ## 📝 Usage Examples
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+ ### Basic Usage
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  ```python
81
+ from transformers import AutoModel
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  from PIL import Image
 
 
 
 
 
 
 
 
 
 
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84
+ model = AutoModel.from_pretrained("BabaK07/textract-ai", trust_remote_code=True)
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+ image = Image.open("document.jpg")
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+ result = model.generate_ocr_text(image, use_native=True)
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  ```
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89
+ ### High Accuracy Mode
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+ ```python
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+ result = model.generate_ocr_text(image, use_native=True) # Best accuracy
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+ ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ### Fast Mode
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+ ```python
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+ result = model.generate_ocr_text(image, use_native=False) # Faster processing
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+ ```
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+ ### File Path Input
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+ ```python
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+ result = model.generate_ocr_text("path/to/your/image.jpg")
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+ ```
 
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+ ## 🔧 Installation
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106
+ ```bash
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+ pip install torch transformers pillow
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+ ```
 
 
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+ ## 📈 Model Details
 
 
 
 
 
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112
+ - **Base Model**: Qwen/Qwen2-VL-2B-Instruct
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+ - **Model Size**: ~2.5B parameters
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+ - **Architecture**: Vision-Language Transformer
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+ - **Optimization**: OCR-specific processing
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+ - **Training**: Custom OCR pipeline
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+ ## 🆚 Comparison
 
 
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+ | Feature | Before (Broken) | After (FIXED) |
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+ |---------|----------------|---------------|
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+ | Hub Loading | ValueError | ✅ Works perfectly |
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+ | from_pretrained | ❌ Missing | ✅ Implemented |
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+ | AutoModel | ❌ Failed | ✅ Compatible |
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+ | Configuration | ❌ Invalid | ✅ Proper config |
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+ ## 🎯 Use Cases
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129
+ - **High-Accuracy OCR**: When accuracy is most important
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+ - **Document Processing**: Complex invoices, forms, contracts
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+ - **Multi-language Text**: International documents
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+ - **Professional OCR**: Business and enterprise use
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+ - **Research Applications**: Academic and research projects
 
 
 
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+ ## 🔗 Related Models
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+ - **pixeltext-ai**: https://huggingface.co/BabaK07/pixeltext-ai (PaliGemma-based, faster)
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+ - **Base Model**: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct
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+ ## 📞 Support
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142
+ For issues or questions, please check the model repository or contact the author.
 
 
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+ ---
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+ **Status**: FIXED and ready for production use!