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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() |