Unsupervised multi-language handwritten text line segmentation

Abstract

Text Lines Segmentation (TLS) affects the performance of manuscript Text Recognition (MTR) systems from document images. At the same time, the TLS task consists of two tasks: the first is Text Lines Localization (TLL) and the second is the Search of the Path that Divides neighboring Lines (SPDL) of handwritten text. The TLS task depends on the type of language, author’s writing style, pen type and document quality. In this paper, Projected Energy Map with Alpha blending (PEM-Alpha) is presented as an unsupervised method for the TLL task, which can work with lines that are touching or overlapping. In addition, SPDL-GA is proposed as a method for SPDL task which finds the line that best splits the text. The experimentation is carried out with a standard collection of historical multilingual documents. Through experimentation it is demostrated that the proposed methods outperform other state-of-the-art methods, even in documents with mixed languages. In addition, few parameters required by PEM-Alpha and SPDL-GA are automatically calculated.