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| import gradio as gr | |
| from transformers import TrOCRProcessor, VisionEncoderDecoderModel | |
| import requests | |
| from PIL import Image | |
| processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") | |
| model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten") | |
| # load image examples | |
| urls = ['https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg', 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSoolxi9yWGAT5SLZShv8vVd0bz47UWRzQC19fDTeE8GmGv_Rn-PCF1pP1rrUx8kOjA4gg&usqp=CAU', | |
| 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRNYtTuSBpZPV_nkBYPMFwVVD9asZOPgHww4epu9EqWgDmXW--sE2o8og40ZfDGo87j5w&usqp=CAU'] | |
| for idx, url in enumerate(urls): | |
| image = Image.open(requests.get(url, stream=True).raw) | |
| image.save(f"image_{idx}.png") | |
| def process_image(image): | |
| # prepare image | |
| pixel_values = processor(image, return_tensors="pt").pixel_values | |
| # generate (no beam search) | |
| generated_ids = model.generate(pixel_values) | |
| # decode | |
| generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| return generated_text | |
| title = "TrOCR + EN_ICR" | |
| description = "Demo for handwritten TrOCR" | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.10282'>TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models</a> | <a href='https://github.com/microsoft/unilm/tree/master/trocr'>Github Repo</a></p>" | |
| examples =[["img_hw_0.png"], ["img_hw_1.png"]] | |
| iface = gr.Interface(fn=process_image, | |
| inputs=gr.inputs.Image(type="pil"), | |
| outputs=gr.outputs.Textbox(), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples) | |
| iface.launch(debug=True) | |