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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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from craft_text_detector import ( |
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read_image, |
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load_craftnet_model, |
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load_refinenet_model, |
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get_prediction, |
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export_detected_regions, |
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export_extra_results, |
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empty_cuda_cache |
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) |
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from craft_text_detector import Craft |
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import torch |
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import numpy as np |
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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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craft = Craft(output_dir=None, |
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crop_type="poly", |
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export_extra=False, |
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link_threshold=0.1, |
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text_threshold=0.3, |
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cuda=torch.cuda.is_available()) |
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urls = ['https://cdn.shopify.com/s/files/1/0275/6457/2777/files/Penwritten_2048x.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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img = np.array(image) |
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prediction_result = craft.detect_text(img) |
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text = [] |
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for i,j in enumerate(prediction_result['boxes']): |
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roi = img[int(prediction_result['boxes'][i][0][1]): int(prediction_result['boxes'][i][2][1]), |
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int(prediction_result['boxes'][i][0][0]): int(prediction_result['boxes'][i][2][0])] |
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image = Image.fromarray(roi).convert("RGB") |
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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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text.append(generated_text) |
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print('line ' + str(i) + ' has been recoginized') |
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generated_text = ('\n').join(text) |
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return generated_text |
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title = "Interactive demo: 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 on IAM, a dataset of annotated handwritten images. To use it, simply upload an image or use the example image 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"]] |
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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) |
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