Unsupervised Block Covering Analysis for Text-Line Segmentation of Arabic Ancient Handwritten Document Images

This paper presents a new method for automatic text-line extraction from Arabic historical handwritten documents presenting an overlapping and multi-touching characters problems. Our approach is based on block covering analysis using unsupervised technique. This algorithm performs firstly a statistical block analysis which computes the optimal number of document decomposition into vertical strips. Then, our algorithm achieves a fuzzy base line detection using fuzzy C-means algorithm. Finally, blocks are assigned to its corresponding lines. Experiment results show that the proposed method achieves high accuracy about 95% for detecting text lines in Arabic historical handwritten document images written with different scripts.