1. Overview

This application utilizes OpenCV, Tesseract, and Tkinter to extract text from images. It allows users to upload an image, convert it to a suitable format for OCR, extract text, and save the text into a DOCX file. The application features a graphical user interface (GUI) for user interaction.

2. Key Libraries Used

• OpenCV (cv2): For image processing (grayscale conversion, thresholding).
• Pytesseract: For Optical Character Recognition (OCR) to extract text.
• PIL: For opening and saving images.
• Tkinter: For the file dialog and GUI components.
• Docx (python-docx): To save extracted text into a Word document.

3. Functionality Breakdown

• Image Upload:

  • Function: upload_image()
  • A file dialog allows users to select an image, which is saved as process.jpg for further processing.

• Image Preprocessing:

  • Grayscale Conversion: The image is converted to grayscale for better OCR accuracy.
  • Thresholding: The image is converted to binary (black and white) to improve text recognition.

• OCR (Text Extraction):

  • Tesseract Configuration: OCR is performed using Tesseract with custom settings optimized for text block recognition (--oem 3 --psm 6).

• Text Display and Save:

  • After extracting text, it is displayed in a Tkinter text box. The user can save the text as a .docx file using the python-docx library.

• Loading Window:

  • A loading window with a progress bar is shown during the OCR process to indicate that the application is working.

4. Code Flow

1. The user uploads an image.
2. The image is processed into grayscale and binary formats for OCR.
3. A loading window appears while OCR is running.
4. The extracted text is displayed, and the user can save it to a Word document.

5. User Interface

• File Dialog: To upload the image.
• Text Box: To display and edit extracted text.
• Buttons: For saving the text and exiting the app.
• Progress Bar: To indicate the OCR processing status.

6. Error Handling

• Error messages are shown if there are issues during image processing or saving the DOCX file, guiding the user in troubleshooting.

7. Potential Enhancements

• Advanced Preprocessing: Further image enhancement could improve OCR accuracy, especially for low-quality images.
• Format Support: Support for more file formats like PDFs could be added.
• Performance: Optimizations such as image resizing or multi-threading could be implemented for faster processing.

8. Conclusion

This application successfully integrates OCR, image processing, and text management in a simple GUI. It allows users to upload an image, extract its text, and save it in a DOCX file. There is potential to expand its features and improve processing efficiency.