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import gradio as gr |
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from transformers import TrOCRProcessor, VisionEncoderDecoderModel |
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import requests |
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from PIL import Image |
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processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-str") |
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model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-str") |
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urls = ['https://raw.githubusercontent.com/ku21fan/STR-Fewer-Labels/main/demo_image/1.png', 'https://raw.githubusercontent.com/HCIILAB/Scene-Text-Recognition-Recommendations/main/Dataset_images/LSVT1.jpg', 'https://raw.githubusercontent.com/HCIILAB/Scene-Text-Recognition-Recommendations/main/Dataset_images/ArT2.jpg'] |
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for idx, url in enumerate(urls): |
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image = Image.open(requests.get(url, stream=True).raw) |
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image.save(f"image_{idx}.png") |
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def process_image(image): |
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pixel_values = processor(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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return generated_text |
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title = "Interactive demo: Scene Text Recognition with TrOCR" |
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description = "Demo for Microsoft's TrOCR, an encoder-decoder model consisting of an image Transformer encoder and a text Transformer decoder for state-of-the-art optical character recognition (OCR) on single-text line images. This particular model is fine-tuned for scene text recognition. To use it, simply upload a (single-text line) image or use one of the example images below and click 'submit'. Results will show up in a few seconds." |
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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>" |
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examples =[["image_0.png"], ["image_1.png"], ["image_2.png"]] |
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iface = gr.Interface(fn=process_image, |
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inputs=gr.inputs.Image(type="pil"), |
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outputs=gr.outputs.Textbox(), |
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title=title, |
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description=description, |
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article=article, |
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examples=examples) |
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iface.launch(debug=True) |
