Text Line Extraction Based on Distance Map Features and Dynamic Programming

Abstract

Text Line Segmentation is a basic document layout task that consists in detecting and extracting the text lines present in a document page image. Although considered a basic task, generally, it is a necessary step for Handwritten Text Recognition (HTR) higher level tasks. Most state of the art automatic text recognition, text-to-line image alignment and key word spotting systems require it due to their need for isolated text line images as input. Traditionally most Text Line Segmentation approaches cover both detection and extraction sub steps. However, the community has recently shifted its focus to tackle independently the baseline detection in document images. This shift generates the need for extraction methods that use these detected baselines as input. In this paper, a binarization free dynamic programming approach that generates an equidistant text line extraction polygon is presented. The approach performs this calculation, based on the information provided by priorly detected text baselines and automatically generated foreground pixels distance maps. We evaluate our approach both in a synthetic competition corpus and in a challenging real handwritten text recognition task corpus. We evaluate it not only at the graphical error level but also the impact it produces on an HTR task trained with the line images it yields. We compare our solution with other solutions ranging from the actual human reviewed ground-truth polygons to simpler automatic generated rectangle areas.