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import re |
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import jaconv |
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import gradio as gr |
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from transformers import AutoTokenizer, AutoFeatureExtractor, VisionEncoderDecoderModel |
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from PIL import Image |
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import torch |
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import spaces, time |
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tokenizer = AutoTokenizer.from_pretrained("kha-white/manga-ocr-base") |
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model = VisionEncoderDecoderModel.from_pretrained("kha-white/manga-ocr-base") |
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model.to("cuda") |
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feature_extractor = AutoFeatureExtractor.from_pretrained("kha-white/manga-ocr-base") |
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examples = ["00.jpg", "01.jpg", "02.jpg", "03.jpg", "04.jpg", "05.jpg", "06.jpg", "07.jpg", "08.jpg", "09.jpg", "10.jpg", "11.jpg"] |
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def post_process(text): |
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text = ''.join(text.split()) |
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text = text.replace('…', '...') |
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text = re.sub('[・.]{2,}', lambda x: (x.end() - x.start()) * '.', text) |
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text = jaconv.h2z(text, ascii=True, digit=True) |
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return text |
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@spaces.GPU |
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def manga_ocr(img): |
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img = img.convert('L').convert('RGB') |
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pixel_values = feature_extractor(img, return_tensors="pt").pixel_values.to("cuda") |
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start_time = time.time() |
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output = model.generate(pixel_values)[0] |
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print("Time taken for OCR:", time.time() - start_time) |
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text = tokenizer.decode(output, skip_special_tokens=True) |
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text = post_process(text) |
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return text |
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iface = gr.Interface( |
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fn=manga_ocr, |
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inputs=gr.Image(type='pil'), |
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outputs="text", |
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title="Manga OCR", |
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description="Extract Manga in lighting speed ⚡", |
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) |
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iface.launch() |
