Combining Learned Script Points and Combinatorial Optimization for Text Line Extraction
Joan Pastor-Pellicer|Angelika Garz|Rolf Ingold|María José Castro-Bleda
Complex layouts, curved text lines, heterogeneous background, noise, and clutter still render line extraction in the context of historical documents a challenging task where traditional methods do not excel. We propose novel method with two contributions: first, text-specific interest points extracted by supervised machine learning; second, reformulating problem bottom-up aggregation as noise-robust combinatorial optimization. In final step, unsupervised clustering eliminates invalid lines. Building on top posing global optimization problem, we can detect lines arbitrary orientation curvature, are robust to noise clutter. Experimental evaluations IAM Saint Gall dataset show promising results.