Ruling-based table analysis for noisy handwritten documents
Proceedings of the 4th International Workshop on Multilingual OCR
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The paper presents a clutter detection and removal algorithm for complex document images. The distance transform based approach is independent of clutter's position, size, shape and connectivity with text. Features are based on a residual image obtained by analysis of the distance transform and clutter elements, if present, are identified with an SVM classifier. Removal is restrictive, so text attached to the clutter is not deleted in the process. The method was tested on a collection of degraded and noisy, machine-printed and handwritten Arabic and English text documents. Results show pixel-level accuracies of 97.5% and 95% for clutter detection and removal respectively. This approach was also extended with a noise detection and removal model for documents having a mix of clutter and salt-n-pepper noise.