Text line segmentation using Viterbi algorithm for the palm leaf manuscripts of Dai

Ge Peng, Pengfei Yu, Haiyan Li, Lesheng He
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引用次数: 13

Abstract

The text line segmentation process is a key step in an optical character recognition (OCR) system. Several common approaches, such as projection-based methods and stochastic methods, have been put forward to fulfill this task. However, most of existing methods cannot be directly applied to process the palm leaf manuscripts of Dai which the images have poor quality and include smudges, creases, stroke deformation and character touching. To solve this problem, an improved Viterbi algorithm based on Hidden Markov Model (HMM) is proposed to find all possible segmentation paths firstly. And then, a path filtering method is used to detect the optimal paths for the segmented text blocks. The performance of the method is compared with relevant methods and the experimental results demonstrate the effectiveness of the proposed method.
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基于Viterbi算法的傣族棕榈叶手稿文本线分割
文本行分割是光学字符识别(OCR)系统的关键步骤。为了完成这一任务,人们提出了几种常用的方法,如基于投影的方法和随机方法。然而,现有的方法大多不能直接用于傣族棕榈叶手稿的处理,其图像质量较差,存在污迹、折痕、笔画变形和文字触碰等问题。为了解决这一问题,提出了一种基于隐马尔可夫模型(HMM)的改进Viterbi算法,首先找到所有可能的分割路径。然后,采用路径滤波方法检测文本块的最优路径。将该方法的性能与相关方法进行了比较,实验结果证明了该方法的有效性。
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