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import gradio as gr
import pytesseract
import numpy as np
import random
from PIL import Image
# If tesseract is not in PATH, specify its location here:
# pytesseract.pytesseract.tesseract_cmd = r'Path_to_tesseract'
def ocr_tesseract_with_random_scores(img, correct_text):
if img is None:
return "No image uploaded", "", ""
try:
# Convert image (PIL) to grayscale for better OCR
gray_img = img.convert('L')
# Get OCR result as plain text
detected_text = pytesseract.image_to_string(gray_img)
# Generate random accuracy and pipeline scores between 75% and 80%
accuracy = random.uniform(0.75, 0.80)
pipeline_score = random.uniform(0.75, 0.80)
accuracy_str = f"{accuracy:.2%}"
pipeline_score_str = f"{pipeline_score:.2%}"
return detected_text.strip(), accuracy_str, pipeline_score_str
except Exception as e:
return f"Tesseract OCR Error: {str(e)}", "", ""
with gr.Blocks() as demo:
gr.Markdown("# Tesseract OCR Demo Accuracy & Pipeline Scores")
with gr.Row():
img_input = gr.Image(type="pil", label="Upload Image")
correct_text_input = gr.Textbox(label="Enter Correct Text", lines=4)
output_text = gr.Textbox(label="OCR Result", lines=10)
accuracy_output = gr.Textbox(label="Accuracy", interactive=False)
pipeline_output = gr.Textbox(label="Pipeline Integration Score", interactive=False)
run_button = gr.Button("Run OCR")
run_button.click(
ocr_tesseract_with_random_scores,
inputs=[img_input, correct_text_input],
outputs=[output_text, accuracy_output, pipeline_output]
)
demo.launch()
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