Text line segmentation is one of the pre-stages of modern optical character recognition systems. The algorithmic approach proposed by this paper has been designed for this exact purpose. Its main characteristic is the combination of two different techniques, morphological image operations and horizontal histogram projections. The method was developed to be applied on a historic data collection that commonly features quality issues, such as degraded paper, blurred text, or curved text lines. For that reason, the segmenter in question could be of particular interest for cultural institutions, such as libraries, archives, museums, ..., that want access to robust line bounding boxes for a given historic document. Because of the promising segmentation results that are joined by low computational cost, the algorithm was incorporated into the OCR pipeline of the National Library of Luxembourg, in the context of the initiative of reprocessing their historic newspaper collection. The general contribution of this paper is to outline the approach and to evaluate the gains in terms of accuracy and speed, comparing it to the segmentation algorithm bundled with the used open source OCR software.
翻译:文本线分解是现代光学字符识别系统的一个前阶段。本文件提议的算法方法就是为这一确切目的设计的。其主要特征是两种不同的技术、形态图象操作和横向直方图预测相结合。该方法是用于历史数据收集的,通常具有质量问题,如纸张退化、文字模糊或曲线文字线。因此,有关分解器对于希望为某一历史文件获得稳健线条框的文化机构,如图书馆、档案馆、博物馆.可能特别感兴趣。由于计算成本低而带来的有希望的分解结果,该算法被纳入卢森堡国家图书馆的OCR管道,这是在对历史报纸收藏进行再处理的主动行动中。本文的一般贡献是概述方法,评价准确性和速度方面的成果,将它与与所使用的开放源OCR软件捆绑的分解算法进行比较。