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Advanced OCR with Tesseract

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Abstract

Advanced OCR with Tesseract is a Python project that leverages the Tesseract OCR engine for high-accuracy Optical Character Recognition. The application performs image preprocessing, text extraction, and post-processing, demonstrating the use of open-source OCR for document analysis.

Prerequisites

  • Python 3.8 or above
  • A code editor or IDE
  • Basic understanding of image processing
  • Required libraries: pytesseractpytesseract, opencv-pythonopencv-python, PillowPillow
  • Tesseract OCR installed (installation guide)

Before you Start

Install Python, Tesseract, and the required libraries:

Install dependencies
pip install pytesseract opencv-python pillow
Install dependencies
pip install pytesseract opencv-python pillow

Getting Started

Create a Project

  1. Create a folder named advanced-ocr-tesseractadvanced-ocr-tesseract.
  2. Open the folder in your code editor or IDE.
  3. Create a file named advanced_ocr_with_tesseract.pyadvanced_ocr_with_tesseract.py.
  4. Copy the code below into your file.

Write the Code

⚙️ Advanced OCR with Tesseract
Advanced OCR with Tesseract
"""
Advanced OCR with Tesseract
 
This project demonstrates advanced Optical Character Recognition (OCR) using Tesseract and Python. It supports multi-language recognition, image preprocessing, batch processing, and saving results to text files. Includes CLI for batch and single image OCR.
 
Requirements:
pip install pytesseract pillow
Make sure Tesseract is installed and in your PATH
 
Example usage:
python advanced_ocr_with_tesseract.py --image sample.png --lang eng --out result.txt
python advanced_ocr_with_tesseract.py --folder images/ --lang eng --out results/
"""
import pytesseract
from PIL import Image, ImageFilter, ImageEnhance
import os
import argparse
 
def preprocess_image(image_path):
    try:
        img = Image.open(image_path)
        img = img.convert('L')
        img = img.filter(ImageFilter.MedianFilter())
        enhancer = ImageEnhance.Contrast(img)
        img = enhancer.enhance(2)
        return img
    except Exception as e:
        print(f"Error processing {image_path}: {e}")
        return None
 
def ocr_image(image_path, lang='eng', out_path=None):
    img = preprocess_image(image_path)
    if img is None:
        return ""
    text = pytesseract.image_to_string(img, lang=lang)
    if out_path:
        with open(out_path, 'w', encoding='utf-8') as f:
            f.write(text)
    return text
 
def batch_ocr(folder, lang='eng', out_folder=None):
    results = {}
    for filename in os.listdir(folder):
        if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.tiff')):
            path = os.path.join(folder, filename)
            out_path = None
            if out_folder:
                os.makedirs(out_folder, exist_ok=True)
                out_path = os.path.join(out_folder, filename + '.txt')
            results[filename] = ocr_image(path, lang, out_path)
    return results
 
def main():
    parser = argparse.ArgumentParser(description="Advanced OCR with Tesseract")
    parser.add_argument('--image', type=str, help='Path to image file for OCR')
    parser.add_argument('--folder', type=str, help='Path to folder for batch OCR')
    parser.add_argument('--lang', type=str, default='eng', help='Language for OCR')
    parser.add_argument('--out', type=str, help='Output file or folder')
    args = parser.parse_args()
 
    if args.image:
        text = ocr_image(args.image, args.lang, args.out)
        print(text)
    elif args.folder:
        batch_ocr(args.folder, args.lang, args.out)
        print(f"Batch OCR completed. Results saved to {args.out}")
    else:
        parser.print_help()
 
if __name__ == "__main__":
    main()
Advanced OCR with Tesseract
"""
Advanced OCR with Tesseract
 
This project demonstrates advanced Optical Character Recognition (OCR) using Tesseract and Python. It supports multi-language recognition, image preprocessing, batch processing, and saving results to text files. Includes CLI for batch and single image OCR.
 
Requirements:
pip install pytesseract pillow
Make sure Tesseract is installed and in your PATH
 
Example usage:
python advanced_ocr_with_tesseract.py --image sample.png --lang eng --out result.txt
python advanced_ocr_with_tesseract.py --folder images/ --lang eng --out results/
"""
import pytesseract
from PIL import Image, ImageFilter, ImageEnhance
import os
import argparse
 
def preprocess_image(image_path):
    try:
        img = Image.open(image_path)
        img = img.convert('L')
        img = img.filter(ImageFilter.MedianFilter())
        enhancer = ImageEnhance.Contrast(img)
        img = enhancer.enhance(2)
        return img
    except Exception as e:
        print(f"Error processing {image_path}: {e}")
        return None
 
def ocr_image(image_path, lang='eng', out_path=None):
    img = preprocess_image(image_path)
    if img is None:
        return ""
    text = pytesseract.image_to_string(img, lang=lang)
    if out_path:
        with open(out_path, 'w', encoding='utf-8') as f:
            f.write(text)
    return text
 
def batch_ocr(folder, lang='eng', out_folder=None):
    results = {}
    for filename in os.listdir(folder):
        if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.tiff')):
            path = os.path.join(folder, filename)
            out_path = None
            if out_folder:
                os.makedirs(out_folder, exist_ok=True)
                out_path = os.path.join(out_folder, filename + '.txt')
            results[filename] = ocr_image(path, lang, out_path)
    return results
 
def main():
    parser = argparse.ArgumentParser(description="Advanced OCR with Tesseract")
    parser.add_argument('--image', type=str, help='Path to image file for OCR')
    parser.add_argument('--folder', type=str, help='Path to folder for batch OCR')
    parser.add_argument('--lang', type=str, default='eng', help='Language for OCR')
    parser.add_argument('--out', type=str, help='Output file or folder')
    args = parser.parse_args()
 
    if args.image:
        text = ocr_image(args.image, args.lang, args.out)
        print(text)
    elif args.folder:
        batch_ocr(args.folder, args.lang, args.out)
        print(f"Batch OCR completed. Results saved to {args.out}")
    else:
        parser.print_help()
 
if __name__ == "__main__":
    main()

Example Usage

Run the OCR
python advanced_ocr_with_tesseract.py
Run the OCR
python advanced_ocr_with_tesseract.py

Explanation

Key Features

  • Image Preprocessing: Uses OpenCV and Pillow for denoising, thresholding, and resizing.
  • Tesseract OCR: Employs open-source OCR for text extraction.
  • Post-Processing: Cleans and formats extracted text.
  • Error Handling: Validates inputs and manages exceptions.
  • CLI Interface: Interactive command-line usage.

Code Breakdown

  1. Import Libraries and Preprocess Image
advanced_ocr_with_tesseract.py
import cv2
from PIL import Image
import pytesseract
advanced_ocr_with_tesseract.py
import cv2
from PIL import Image
import pytesseract
  1. Image Preprocessing Function
advanced_ocr_with_tesseract.py
def preprocess_image(img_path):
    img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
    img = cv2.resize(img, (128, 32))
    img = cv2.GaussianBlur(img, (3, 3), 0)
    _, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    return img
advanced_ocr_with_tesseract.py
def preprocess_image(img_path):
    img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
    img = cv2.resize(img, (128, 32))
    img = cv2.GaussianBlur(img, (3, 3), 0)
    _, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    return img
  1. Text Extraction with Tesseract
advanced_ocr_with_tesseract.py
def extract_text(img):
    pil_img = Image.fromarray(img)
    text = pytesseract.image_to_string(pil_img)
    return text
advanced_ocr_with_tesseract.py
def extract_text(img):
    pil_img = Image.fromarray(img)
    text = pytesseract.image_to_string(pil_img)
    return text
  1. CLI Interface and Error Handling
advanced_ocr_with_tesseract.py
def main():
    print("Advanced OCR with Tesseract")
    img_path = input("Enter image path: ")
    try:
        img = preprocess_image(img_path)
        text = extract_text(img)
        print(f"Extracted Text: {text}")
    except Exception as e:
        print(f"Error: {e}")
 
if __name__ == "__main__":
    main()
advanced_ocr_with_tesseract.py
def main():
    print("Advanced OCR with Tesseract")
    img_path = input("Enter image path: ")
    try:
        img = preprocess_image(img_path)
        text = extract_text(img)
        print(f"Extracted Text: {text}")
    except Exception as e:
        print(f"Error: {e}")
 
if __name__ == "__main__":
    main()

Features

  • Open-Source OCR: High-accuracy text extraction
  • Modular Design: Separate functions for preprocessing and extraction
  • Error Handling: Manages invalid inputs and exceptions
  • Production-Ready: Scalable and maintainable code

Next Steps

Enhance the project by:

  • Supporting batch OCR for multiple images
  • Creating a GUI with Tkinter or a web app with Flask
  • Supporting multilingual OCR
  • Adding evaluation metrics (CER, WER)
  • Unit testing for reliability

Educational Value

This project teaches:

  • Image Processing: Preprocessing for OCR
  • Open-Source Tools: Using Tesseract for text extraction
  • Software Design: Modular, maintainable code
  • Error Handling: Writing robust Python code

Real-World Applications

  • Document Digitization
  • Accessibility Tools
  • Data Entry Automation
  • Content Management

Conclusion

Advanced OCR with Tesseract demonstrates how to use open-source OCR for high-accuracy text extraction from images. With modular design and extensibility, this project can be adapted for real-world document analysis and automation. For more advanced projects, visit Python Central Hub.

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