Measuring Time and Accuracy Performance of pytesseract
By JoeVu, at: Feb. 11, 2023, 5:20 p.m.
Measuring Time and Accuracy Performance of pytesseract
Introduction
pytesseract
is a popular OCR library in Python, but how well does it perform in terms of speed and accuracy? This blog post will guide you through setting up tests to measure both time performance and accuracy of the pytesseract
package.
Setup
First, ensure you have pytesseract
and its dependencies installed:
pip install pytesseract
pip install pillow
sudo apt-get install tesseract-ocr # for ubuntu
Preparing Test Images
Gather a diverse set of images with varying text complexity, sizes, fonts, and noise levels.
Save these images in a directory named test_images
.
Measuring Time Performance
Create a Python script to measure the time taken to process each image.
import time
from PIL import Image
import pytesseract
import os
def measure_time(image_path):
start_time = time.time()
image = Image.open(image_path)
text = pytesseract.image_to_string(image)
end_time = time.time()
return end_time - start_time
image_dir = 'test_images'
times = []
for image_name in os.listdir(image_dir):
image_path = os.path.join(image_dir, image_name)
time_taken = measure_time(image_path)
times.append((image_name, time_taken))
print(f"Time taken for {image_name}: {time_taken:.2f} seconds")
average_time = sum(time for _, time in times) / len(times)
print(f"Average time per image: {average_time:.2f} seconds")
Measuring Accuracy Performance
To measure accuracy, we need the expected text for each image. Create a dictionary with image names as keys and expected text as values.
expected_result = {
'image1.jpg': 'Expected text for image 1',
'image2.jpg': 'Expected text for image 2',
# Add more images and their expected text
}
def measure_accuracy(image_path, expected_text):
image = Image.open(image_path)
extracted_text = pytesseract.image_to_string(image)
return extracted_text == expected_text
accuracies = []
for image_name, expected_text in expected_result.items():
image_path = os.path.join(image_dir, image_name)
accuracy = measure_accuracy(image_path, expected_text)
accuracies.append((image_name, accuracy))
print(f"Accuracy for {image_name}: {'Correct' if accuracy else 'Incorrect'}")
accuracy_rate = sum(1 for _, accuracy in accuracies if accuracy) / len(accuracies)
print(f"Overall accuracy rate: {accuracy_rate:.2%}")
Conclusion
By following this approach, you can effectively measure both the time performance and accuracy performance of the pytesseract
OCR library. This helps in understanding its efficiency and reliability for your specific use cases.
Next Steps
- Advanced Configurations: Explore
pytesseract
options for language selection and configuration settings.
- Preprocessing: Implement image preprocessing techniques to improve OCR accuracy.
- Comparison: Compare
pytesseract
with other OCR libraries and APIs for a comprehensive analysis. You can find one of our blost post here
By thoroughly testing pytesseract
, you can ensure it meets your performance and accuracy requirements for OCR tasks.