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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-handwritten") |
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model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten") |
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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 = "Transforme(encoder-decoder) based Text OCR" |
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description = "Demo for Microsoft's TrOCR, an encoder-decoder model \ |
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consisting of an image Transformer encoder and a text Transformer \ |
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decoder for state-of-the-art optical character recognition (OCR) on \ |
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single-text line images. This particular model is fine-tuned on IAM, \ |
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a dataset of annotated handwritten images." |
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.10282'>Transformer 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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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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iface.launch(debug=False) |
