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--- |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- cer |
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pipeline_tag: image-to-text |
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--- |
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```markdown |
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# OCR with Hugging Face Transformers |
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``` |
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This repository demonstrates how to perform Optical Character Recognition (OCR) using the Hugging Face Transformers library. The code in this repository utilizes a pretrained model for OCR on images. |
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## Prerequisites |
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Before you can run the code, you'll need to install the required libraries. You can do this with `pip`: |
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```python |
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pip install transformers |
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pip install pillow |
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``` |
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## Usage |
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You can use the provided code to perform OCR on images. Here are the basic steps: |
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1. Import the necessary libraries: |
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```python |
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from transformers import VisionEncoderDecoderModel |
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from transformers import TrOCRProcessor, VisionEncoderDecoderModel |
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from PIL import Image |
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import requests |
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``` |
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2. Load the pretrained OCR model and processor: |
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```python |
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model = VisionEncoderDecoderModel.from_pretrained("vanshp123/ocrmnist") |
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processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-stage1') |
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``` |
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3. Load an image for OCR. You can replace `"/content/left_digit_section_4.png"` with the path to your image: |
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```python |
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image = Image.open("/content/left_digit_section_4.png").convert("RGB") |
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``` |
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4. Process the image using the OCR processor and generate the text: |
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```python |
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pixel_values = processor(images=image, return_tensors="pt").pixel_values |
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generated_ids = model.generate(pixel_values) |
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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5. `generated_text` will contain the text recognized from the image. |
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## Example |
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You can use this code as a starting point for your OCR projects. It's important to adapt it to your specific use case and customize it as needed. |
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## License |
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This code uses models from the Hugging Face Transformers library, and you should review their licensing and usage terms for the pretrained models. |
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``` |