A Binarization-Free Clustering Approach to Segment Curved Text Lines in Historical Manuscripts

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 Yet it remains an open problem for handwritten historical documents because of complex layouts on the one hand, such as curved and touching text lines, and binarization problems on the other hand, caused by ornaments, wrinkles,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