Python OCR Image Reader with Tesseract

OCR Extraction with Python

Python OCR Image Reader with Tesseract

💾 1. Install Required Libraries

Use pip to install all required packages:

pip install opencv-python pytesseract pillow numpy

🔍 2. Overview

This Python script reads an image file (e.g., denek.png) containing Turkish text, preprocesses it for better recognition, extracts text using pytesseract, and prints specific fields like Gönderen (Sender) and Kaynak (Source).

📦 3. Check Library Dependencies

def check_libraries():
    necessary_libraries = ["cv2", "pytesseract", "PIL", "pathlib", "os", "numpy"]
    for library in necessary_libraries:
        try:
            __import__(library)
            print(f"{library} is installed.")
        except ImportError:
            print(f"{library} is not installed. Please install it using pip: pip install {library}")
            return False
    return True

📁 4. Image File Path (Default)

The script expects denek.png to be in the same folder as the script:

image_path = Path("denek.png")

🖼️ 5. Preprocessing the Image

  • Convert to grayscale
  • Apply binary thresholding
  • Save temporary image for OCR
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, binary_image = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV)
cv2.imwrite("preprocessed_image.png", binary_image)

🧠 6. OCR & Text Extraction

text = pytesseract.image_to_string(Image.open("preprocessed_image.png"), lang='tur')

🧾 7. Extract & Print Information

print(f"Gönderen: {extracted_data.get('Gönderen', 'Not found')}")
print(f"Kaynak: {extracted_data.get('Kaynak', 'Not found')}")

🧹 8. Clean Up

Temporary file is deleted:

if preprocessed_image_path.is_file():
    preprocessed_image_path.unlink()

🔁 9. Full Code (With Default Image Path)

import cv2
import pytesseract
from PIL import Image
from pathlib import Path
import os
import numpy as np

def check_libraries():
    necessary_libraries = ["cv2", "pytesseract", "PIL", "pathlib", "os", "numpy"]
    for library in necessary_libraries:
        try:
            __import__(library)
            print(f"{library} is installed.")
        except ImportError:
            print(f"{library} is not installed. Please install it using pip: pip install {library}")
            return False
    return True

def pil_to_cv2(pil_image):
    return cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)

def main():
    pytesseract.pytesseract.tesseract_cmd = r"C:/Program Files/Tesseract-OCR/tesseract.exe"
    image_path = Path("denek.png")

    try:
        print(f"Looking for image at: {image_path}")
        if not image_path.is_file():
            raise FileNotFoundError(f"Image not found at: {image_path}")

        pil_image = Image.open(image_path)
        image = pil_to_cv2(pil_image)

        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        _, binary_image = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV)

        preprocessed_image_path = Path("preprocessed_image.png")
        cv2.imwrite(str(preprocessed_image_path), binary_image)

        text = pytesseract.image_to_string(Image.open(preprocessed_image_path), lang='tur')
        lines = text.split('\n')

        extracted_data = {}
        for line in lines:
            if ':' in line:
                key, value = line.split(':', 1)
                extracted_data[key.strip()] = value.strip()

        print(f"Gönderen: {extracted_data.get('Gönderen', 'Not found')}")
        print(f"Kaynak: {extracted_data.get('Kaynak', 'Not found')}")

        if preprocessed_image_path.is_file():
            preprocessed_image_path.unlink()

    except Exception as e:
        print(f"Error: {e}")

if __name__ == "__main__":
    if check_libraries():
        main()
    else:
        print("Please install the necessary libraries and try again.")
© 2025 - OCR Python Project | Default image path: denek.png

Comments

Post a Comment

Popular posts from this blog

CIFAR-10 Dataset Classification Using Convolutional Neural Networks (CNNs) With PyTorch

CIFAR-10 Image Classification with CNN (PyTorch) - Complete Guide CIFAR-10 Image Classification Using CNN with PyTorch This comprehensive guide demonstrates how to classify images from the CIFAR-10 dataset using Convolutional Neural Networks (CNNs) built with PyTorch. The project covers the entire machine learning pipeline from data loading to model deployment, including advanced techniques for improving performance. 🚀 Key Features Complete PyTorch implementation Data augmentation techniques Multiple CNN architectures Training visualization Model evaluation metrics Hyperparameter tuning Model saving/loading Deployment options 📊 Performance Baseline model: ~70% accuracy Improved mod...
Read more

Radial Basis Function Networks with PyTorch

Advanced Radial Basis Function Networks with PyTorch Advanced Radial Basis Function Networks (RBFNs) with PyTorch Introduction to RBF Networks Radial Basis Function Networks are a type of artificial neural network that uses radial basis functions as activation functions. They are particularly effective for pattern recognition and function approximation problems. Key Characteristics Three-layer architecture : Input layer, hidden RBF layer, and linear output layer Localized activation : Each neuron in the hidden layer responds only to inputs near its center Fast training : Often requires fewer iterations than multilayer perceptrons Universal approximation : Can approximate any continuous function given enough hidden units Mathematical Foundation : The RBF network implements a function of the form: f(x) = Σ w_i * φ(||x - c_i||) ...
Read more

Long-short-term-memory (LSTM) Word Prediction With PyTorch

LSTM Text Generation with PyTorch - Complete Guide LSTM Text Generation with PyTorch A comprehensive guide to building word-level language models with Long Short-Term Memory networks Introduction to LSTM Text Generation Long Short-Term Memory (LSTM) networks are a special kind of recurrent neural network (RNN) capable of learning long-term dependencies. They are particularly useful for sequence prediction problems like text generation, where the context from previous words is crucial for predicting the next word. Key Concepts Word-level modeling : Predicts the next word given previous words Embeddings : Dense vector representations of words ...
Read more