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.