Hebrew Handwriting Recognition and Spell Checking

This project focuses on Hebrew handwriting recognition and spell checking using an autocorrect library. It provides a Python-based solution for recognizing handwritten Hebrew text and applying spell checking using an external library. The project utilizes image processing techniques and a neural network model for character recognition. Users have the option to improve the handwriting recognition accuracy by providing their own handwriting samples and retraining the neural network.

Getting Started

To use this project, follow these steps:

1. Clone the repository to your local machine:

2. Install the required dependencies:

3. Place your input image containing handwritten Hebrew text in the sentences directory.

4. Run the text_recognition.py script:

Features

• Preprocesses input images to enhance text visibility and remove noise.
• Splits text into lines and individual letters for recognition.
• Utilizes a pre-trained neural network model for Hebrew character recognition.
• Applies spell checking using an autocorrect library.

Code Overview

The text_recognition.py script provides the main functionality of the project:

• text_recognition class: Handles image preprocessing, text splitting, character recognition, and spell checking.
• preprocess1 method: Applies initial preprocessing to the input image.
• split_lines method: Segments the text into lines based on contours.
• convert_lines_to_letters method: Converts lines to individual letters and performs character recognition.
• send_to_OCR method: Sends letters to the OCR model for recognition.
• spell_check method: Applies spell checking to the recognized text.

Dependencies

• OpenCV (cv2) for image preprocessing and contour detection.
• TensorFlow and Keras for neural network-based character recognition.
• autocorrect library for spell checking.