Binarization-Free Text Line Segmentation for Historical Documents Based on Interest Point Clustering
Angelika Garz|Andreas Fischer|Robert Sablatnig|Horst Bunke
Segmenting page images into text lines is a crucial pre-processing step for automated reading of historical documents. Challenging issues in this open research field are given \eg by paper or parchment background noise, ink bleed-through, artifacts due to aging, stains, and touching lines. In paper, we present novel binarization-free line segmentation method that robust noise copes with overlapping First, interest points representing parts characters extracted from gray-scale images. Next, word clusters identified high-density regions components such as ascenders descenders separated using seam carving. Finally, generated concatenating neighboring clusters, where neighborhood defined the prevailing orientation words document. An experimental evaluation on Latin manuscript Saint Gall database shows promising results real-world applications terms both accuracy efficiency.