A Binarization-Free Clustering Approach to Segment Curved Text Lines in Historical Manuscripts
Angelika Garz|Andreas Fischer|Horst Bunke|Rolf Ingold
Text line segmentation is one of the main parts document image analysis, it provides crucial information for automated reading, word spotting, alignment between and transcription, or indexing documents. Yet remains an open problem handwritten historical documents because complex layouts on hand, such as curved touching text lines, binarization problems other caused by ornaments, wrinkles, stains, holes, etc. In this paper, we propose a binarization-free clustering method that not only able to cope with but also baseline curvature. Avoiding assumption straight baselines, small interest point clusters are grouped into lines based their local orientation. Experiments conducted artificially distorted images Saint Gall database show promising results.