A Multilevel Text-Line Segmentation Framework for Handwritten Historical Documents

Text-line segmentation is considered as a crucial step of document analysis and recognition systems because its output is considered as the input of recognition systems. Due to the reason that the same handwritten image page has different characteristics, we propose in this paper a multilevel segmentation framework for handwritten historical documents. In this framework, one or many segmentation methods are selected according to the input document features. This framework is tested on the IAM historical database (60 images) and on images from the segmentation competition for handwritten document segmentation held at ICFHR 2010. The evaluation of the segmentation framework is based on several evaluation metrics. The tests show that the proposed framework gives promoting results.