File size: 2,504 Bytes
ef9ba78 16ca38c ef9ba78 16ca38c ef9ba78 16ca38c ef9ba78 16ca38c ef9ba78 16ca38c ef9ba78 16ca38c ef9ba78 16ca38c ef9ba78 16ca38c ef9ba78 16ca38c |
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 |
import gradio as gr
import paddle
from paddleocr import PaddleOCR, draw_ocr
import cv2
from PIL import Image
import numpy as np
# Initialize PaddleOCR for Arabic
ocr = PaddleOCR(use_angle_cls=True, lang='ar')
def ocr_extract_text(image):
if image is None:
return "No image provided", None
# Convert PIL Image to numpy array if needed
if isinstance(image, Image.Image):
image = np.array(image)
# Ensure image is in BGR format for PaddleOCR
if len(image.shape) == 3 and image.shape[2] == 4: # RGBA
image = cv2.cvtColor(image, cv2.COLOR_RGBA2BGR)
elif len(image.shape) == 3 and image.shape[2] == 3: # RGB
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Perform OCR on the uploaded image
result = ocr.ocr(image, cls=True)
if result[0] is None:
return "No text detected in the image", image
# Initialize a list to store detected text
detected_text = []
# Loop through the results and extract text
for line in result[0]: # result[0] contains the OCR results for text regions
text = line[1][0] # Detected text
detected_text.append(text)
# Draw the OCR results on the image
boxes = [line[0] for line in result[0]] # Bounding box coordinates
texts = [line[1][0] for line in result[0]] # Detected text
scores = [line[1][1] for line in result[0]] # Confidence scores
try:
annotated_image = draw_ocr(image, boxes, texts, scores)
except Exception as e:
print(f"Error annotating image: {e}")
annotated_image = image
# Join the detected text with Arabic-compatible line breaks
text_output = "\n".join(detected_text)
return text_output, annotated_image
# Define Gradio interface with Arabic-friendly styling
css = """
.output-text { direction: rtl; text-align: right; }
"""
iface = gr.Interface(
fn=ocr_extract_text,
inputs=gr.Image(type="numpy", label="Upload Image"),
outputs=[
gr.Textbox(label="Extracted Arabic Text", elem_classes=["output-text"]),
gr.Image(label="Annotated Image")
],
title="Arabic OCR Extractor",
description="Upload an Arabic document or image to extract the text using OCR.",
css=css,
examples=[], # You can add example images here
cache_examples=True
)
# For Colab and Hugging Face Spaces compatibility
if __name__ == "__main__":
iface.launch(debug=True, share=True) # share=True enables Colab public URL
|