Text Detection from Images using OpenCV, Tesseract, and Streamlit

This repository contains code for a simple application to detect text from images using Python libraries OpenCV for image processing, Tesseract for Optical Character Recognition (OCR), and Streamlit for creating a user-friendly web application. The application allows users to upload images or capture them via camera input and extracts text present in the images.

Features:

• Text extraction from uploaded images (JPG, PNG, JPEG)
• Camera input for real-time text capture
• Image preprocessing for improved OCR accuracy (grayscale ->conversion, Gaussian blurring, thresholding)
• Text bounding box visualization
• Extracted text displayed in a code block

Requirements

• Python 3.x
• OpenCV (cv2)
• Tesseract (pytesseract)
• Streamlit (streamlit)

Make sure you have these libraries installed in your Python environment before running the application.

Setup

1. Clone this repository to your local machine.
2. Install the required Python libraries by running:

   pip install -r requirements.txt
   

3. Download and install Tesseract OCR. Make sure to add the Tesseract binary path to the script (main.py) where indicated.

Tesseract Path:

• Edit the line pytesseract.pytesseract.tesseract_cmd = 'C:\Program Files\Tesseract-OCR\tesseract.exe' in app.py to point to the correct path of your Tesseract executable on your system.

Usage

Run the application using the following command:

streamlit run click.py

Once the Streamlit server starts, you can access the application via your web browser.

Camera Input

• Go to the "Camera Input" section to use upload image in real time.
• Click on Take Photo to capture an image using your device's camera.
• Once captured, the image will be displayed, and text will be extracted from it.
• The extracted text will be shown below the processed image.

File Input

• Go to the "File Input" section to upload an image from your local machine.
• Supported formats include: .jpg ,.jpeg & .png
• CLick on Browse Files to upload an image from local storage.
• Once uploaded, the image will be displayed, and text will be extracted from it.
• The extracted text will be shown below the processed image.

Folder Structure

• temp/: Temporary directory to store intermediate image files during processing.
• main.py: Main Python script containing the text extraction logic.
• requirements.txt: File containing the required Python libraries.

Authors

• Mohammad Danish Sheikh (GitHub: Danish811)
• Kanchan Dixit (GitHub: kanchan2803)

Contributions

Contributions to improve the functionality, efficiency, or documentation of this application are welcome. Feel free to submit a pull request with your changes.

Thankyou!