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README.md

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+Sure, here's a `readme.md` file for using the code you provided with the Hugging Face Transformers library:
+
+```markdown
+# OCR with Hugging Face Transformers
+
+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.
+
+## Prerequisites
+
+Before you can run the code, you'll need to install the required libraries. You can do this with `pip`:
+
+```bash
+pip install transformers
+pip install pillow
+```
+
+## Usage
+
+You can use the provided code to perform OCR on images. Here are the basic steps:
+
+1. Import the necessary libraries:
+
+```python
+from transformers import VisionEncoderDecoderModel
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+import requests
+```
+
+2. Load the pretrained OCR model and processor:
+
+```python
+model = VisionEncoderDecoderModel.from_pretrained("vanshp123/ocrmnist")
+processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-stage1')
+```
+
+3. Load an image for OCR. You can replace `"/content/left_digit_section_4.png"` with the path to your image:
+
+```python
+image = Image.open("/content/left_digit_section_4.png").convert("RGB")
+```
+
+4. Process the image using the OCR processor and generate the text:
+
+```python
+pixel_values = processor(images=image, return_tensors="pt").pixel_values
+generated_ids = model.generate(pixel_values)
+generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+5. `generated_text` will contain the text recognized from the image.
+
+## Example
+
+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.
+
+## License
+
+This code uses models from the Hugging Face Transformers library, and you should review their licensing and usage terms for the pretrained models.
+
+```