This project is a client side Image Analysis Toolkit for text document Binarization & Segmentation written in TypeScript.

Demo: (https://imganalysis.netlify.app)

Binarization

Implements Otsu [1], Sauvola [2] and GPP [3] Binarization methods for modern/degraded documents.

Segmentation

Implements Text Segmentation method for modern/historical machine-printed documents based on ARLSA [4].

• Open and read Image Files
• Image histogram chart
• Pixel manipulation with canvas
• Image Processing with Web Workers
• Load and process multiple images together
• Load Ground Truth files and evaluate the implemented processing algorithms
• Custom made Tool that:
  • Displays the word segments inside the image (Selects Multiple rectangular areas of the image)
  • Allows to add, delete, move, resize the selections with mouse
  • Drag and move image inside Canvas
  • Zoom image with mouse wheel
  • Keyboard support for above operations
  • Auto-selects the word-boundaries(background pixels, works only on binary images)
  • Opens text input field above each selection for typing the retrieved word
  • Saves selections to local Storage and auto-loads them on start-up
  • Keeps aspect ratio of the selections on window resize
  • Fully Responsive
• Table for viewing the extracted word segments
• Save and Download the extracted word segments to JSON file
• Responsive UI with Angular Material

Reference

1. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Systems, Man, and Cybernetics, vol. 9, pp. 62-66, 1979.

2. J. Sauvola and M. Pietikainen, "Adaptive document image binarization," Pattern Recognition, vol. 33, no. 2, pp. 225-236, February 2000.

3. B. Gatos, I. Pratikakis and S. J. Perantonis, "Adaptive degraded document image binarization," Pattern Recognition, vol. 39, no. 3, pp. 317-327, March 2006.

4. N. Nikolaou, M. Makridis, B. Gatos, N. Stamatopoulos and N. Papamarkos, "Segmentation of historical machine-printed documents using Adaptive Run Length Smoothing and skeleton segmentation paths," Image and Vision Computing, vol. 28, no. 4, pp. 590-604, April 2010.