import pandas as pd import PIL from PIL import Image from PIL import ImageDraw import gradio as gr import torch import easyocr import cv2 import math import numpy as np torch.hub.download_url_to_file('https://i.pinimg.com/originals/45/d0/30/45d03054e15f4be731781eecba7458a4.jpg', 'korean.jpg') def midpoint(x1, y1, x2, y2): x_mid = int((x1 + x2)/2) y_mid = int((y1 + y2)/2) return (x_mid, y_mid) def draw_mask(img, bounds): draw = ImageDraw.Draw(img) for bound in bounds: p0, p1, p2, p3 = bound[0] draw.polygon((*p0, *p1, *p2, *p3, *p0), fill=255) return img def inference(img, lang): if lang == "english": lang = ['en'] elif lang == "chinese": lang = ['ch_sim'] elif lang == "korean": lang = ['ko'] else: lang = ['ja'] reader = easyocr.Reader(lang) bounds = reader.readtext(img.name) im = PIL.Image.open(img.name) #mask = PIL.Image.open(img.name) mask = Image.new("L", im.size, 0) draw_mask(mask, bounds) img_inpainted = cv2.inpaint(im, mask, 7, cv2.INPAINT_NS) #remove_text(im, mask, bounds) lang = "" im.save('result.jpg') mask.save('mask.jpg') img_inpainted.save('inpaint.jpg') return ['result.jpg', 'mask.jpg', 'inpaint.jpg', pd.DataFrame(bounds). iloc[: , 1:2]] title = 'EasyOCR' description = 'Gradio demo for EasyOCR. EasyOCR demo supports 80+ languages.To use it, simply upload your image and choose a language from the dropdown menu, or click one of the examples to load them. Read more at the links below.' article = "

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc. | Github Repo

" css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}" examples = [['korean.jpg',"korean"]] choices = [ "chinese", "english", "japanese", "korean" ] gr.Interface( inference, [gr.inputs.Image(type='file', label='Input'),gr.inputs.Dropdown(choices, type="value", default="korean", label='language')], [gr.outputs.Image(type='file', label='Output'), gr.outputs.Image(type='file', label='Output'), gr.outputs.Image(type='file', label='Output'), gr.outputs.Dataframe()], title=title, description=description, article=article, examples=examples, css=css, enable_queue=True ).launch(debug=True)