Spaces:
Sleeping
Sleeping
File size: 5,466 Bytes
26b554c 7bb7634 26b554c 7bb7634 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 |
import pandas as pd
import PIL
from PIL import Image
from PIL import ImageDraw
import gradio as gr
import easyocr
# Download example images (same as before)
# Download example images
torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/english.png', 'english.png')
torch.hub.download_url_to_file('https://i.imgur.com/mwQFd7G.jpeg', 'Hindi.jpeg')
torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/thai.jpg', 'thai.jpg')
torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/french.jpg', 'french.jpg')
torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/chinese.jpg', 'chinese.jpg')
torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/japanese.jpg', 'japanese.jpg')
torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/korean.png', 'korean.png')
def draw_boxes(image, bounds, color='yellow', width=2):
draw = ImageDraw.Draw(image)
for bound in bounds:
p0, p1, p2, p3 = bound[0]
draw.line([*p0, *p1, *p2, *p3, *p0], fill=color, width=width)
return image
def format_extracted_text(bounds):
return " ".join([text for _, text, _ in bounds])
def highlight_search_results(text, search_query):
if not search_query:
return text, []
pattern = re.compile(re.escape(search_query), re.IGNORECASE)
matches = list(pattern.finditer(text))
highlighted_text = pattern.sub(lambda m: f"**{m.group()}**", text)
return highlighted_text, matches
def inference(img, lang):
reader = easyocr.Reader(lang)
bounds = reader.readtext(img)
im = PIL.Image.open(img)
draw_boxes(im, bounds)
im.save('result.jpg')
extracted_text = format_extracted_text(bounds)
return ['result.jpg', extracted_text]
def search_text(text, search_query):
highlighted_text, matches = highlight_search_results(text, search_query)
if matches:
result = f"Found {len(matches)} occurrence(s) of \"{search_query}\":\n"
for i, match in enumerate(matches, 1):
context_start = max(0, match.start() - 20)
context_end = min(len(text), match.end() + 20)
context = text[context_start:context_end]
result += f"{i}. ...{context}...\n"
else:
result = f"No occurrences of \"{search_query}\" found."
return highlighted_text, result
title = 'Image To Text OCR Converter'
subtitle = 'Extract Hindi/English or both or any Text From Image'
note = """
*Note: This application is being built on the request of IIT R Internship Assignment. It allows users to upload a single image, processes the image to extract text using OCR, and provides a basic search feature.*
*Please keep patience while processing the OCR, as it may take a few seconds to complete.*
"""
alternative_link = "[Alternative: Ready-to-use OCR using Vercel](https://iitr-haq-nawaz-maliks-projects.vercel.app/)"
examples = [
['english.png', ['en', 'hi']],
['Hindi.jpeg', ['hi', 'en']],
['thai.jpg', ['th', 'en', 'hi']],
['french.jpg', ['fr', 'en', 'hi']],
['chinese.jpg', ['ch_sim', 'en', 'hi']],
['japanese.jpg', ['ja', 'en', 'hi']],
['korean.png', ['ko', 'en', 'hi']]
]
css = """
.output_image, .input_image {height: 40rem !important; width: 100% !important;}
.search_results {margin-top: 1rem; padding: 1rem; background-color: #f0f0f0; border-radius: 4px;}
.centered-title {text-align: center; font-size: 2.5em; font-weight: bold; margin-bottom: 0.5em;}
.centered-subtitle {text-align: center; font-size: 1.5em; margin-bottom: 1em;}
.alternative-link {text-align: center; margin-top: 1em; font-style: italic;}
"""
choices = [
"abq", "ady", "af", "ang", "ar", "as", "ava", "az", "be", "bg", "bh", "bho", "bn", "bs", "ch_sim", "ch_tra",
"che", "cs", "cy", "da", "dar", "de", "en", "es", "et", "fa", "fr", "ga", "gom", "hi", "hr", "hu", "id",
"inh", "is", "it", "ja", "kbd", "kn", "ko", "ku", "la", "lbe", "lez", "lt", "lv", "mah", "mai", "mi", "mn",
"mr", "ms", "mt", "ne", "new", "nl", "no", "oc", "pi", "pl", "pt", "ro", "ru", "rs_cyrillic", "rs_latin",
"sck", "sk", "sl", "sq", "sv", "sw", "ta", "tab", "te", "th", "tjk", "tl", "tr", "ug", "uk", "ur", "uz", "vi"
]
with gr.Blocks(css=css) as iface:
gr.Markdown(f"# {title}")
gr.Markdown(f"## {subtitle}")
gr.Markdown(description)
gr.Markdown(note)
with gr.Row():
with gr.Column(scale=2):
input_image = gr.Image(type="filepath", label="Upload Image")
lang_select = gr.CheckboxGroup(choices=choices, label="Select Languages", value=['hi', 'en'])
ocr_button = gr.Button("Perform OCR")
with gr.Column(scale=3):
output_image = gr.Image(type="filepath", label="OCR Result")
extracted_text = gr.Markdown(label="Extracted Text")
search_box = gr.Textbox(label="Search in extracted text")
search_button = gr.Button("Search")
search_results = gr.Markdown(label="Search Results")
ocr_button.click(inference, inputs=[input_image, lang_select], outputs=[output_image, extracted_text])
search_button.click(search_text, inputs=[extracted_text, search_box], outputs=[extracted_text, search_results])
gr.Examples(examples, inputs=[input_image, lang_select])
gr.Markdown(article)
iface.launch() |