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| import re | |
| import jaconv | |
| import gradio as gr | |
| from transformers import AutoTokenizer, AutoFeatureExtractor, VisionEncoderDecoderModel | |
| from PIL import Image | |
| import torch | |
| tokenizer = AutoTokenizer.from_pretrained("kha-white/manga-ocr-base") | |
| model = VisionEncoderDecoderModel.from_pretrained("kha-white/manga-ocr-base") | |
| feature_extractor = AutoFeatureExtractor.from_pretrained("kha-white/manga-ocr-base") | |
| 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"] | |
| def post_process(text): | |
| text = ''.join(text.split()) | |
| text = text.replace('…', '...') | |
| text = re.sub('[・.]{2,}', lambda x: (x.end() - x.start()) * '.', text) | |
| text = jaconv.h2z(text, ascii=True, digit=True) | |
| return text | |
| def manga_ocr(img): | |
| img = img.convert('L').convert('RGB') | |
| pixel_values = feature_extractor(img, return_tensors="pt").pixel_values | |
| output = model.generate(pixel_values)[0] | |
| text = tokenizer.decode(output, skip_special_tokens=True) | |
| text = post_process(text) | |
| return text | |
| iface = gr.Interface( | |
| fn=manga_ocr, | |
| inputs=[gr.inputs.Image(label="Input", type="pil")], | |
| outputs="text", | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="Manga OCR", | |
| description="Optical Character Recognization for Japanese Texts with focus on Mangas. The model is trained by kha-white with Github link: <a href=\"https://github.com/kha-white/manga-ocr\">manga-ocr</a> while the Space App is made by me.", | |
| allow_flagging='never', | |
| examples=examples, | |
| article = "Author: <a href=\"https://huggingface.co/gryan-galario\">Gryan Galario</a>", | |
| ) | |
| iface.launch() |