Multi-oriented Text Recognition in Graphical Documents Using HMM

P. Roy, Sangheeta Roy, U. Pal
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引用次数: 5

Abstract

The text lines in graphical documents (e.g., maps, engineering drawings), artistic documents etc., are often annotated in curve lines to illustrate different locations or symbols. For the optical character recognition of such documents, individual text lines from the documents need to be extracted and recognized. Due to presence of multi-oriented characters in such non-structured layout, word recognition is a challenging task. In this paper, we present an approach towards the recognition of scale and orientation invariant text words in graphical documents using Hidden Markov Models (HMM). First, a line extraction method is applied to segment text lines and the method is based on the foreground and background information of the text components. To effectively utilize the background information, a water reservoir concept is used here. For recognition of curved text lines, a path of sliding window is estimated and features extracted from the sliding window are fed to the HMM system for recognition. Local gradient histogram (LGH) based frame-wise feature is used in HMM. The experimental results are evaluated on a dataset of graphical words and we have obtained encouraging results.
基于HMM的图形文档多方向文本识别
图形文件(如地图、工程图纸)、美术文件等中的文本线通常用曲线加以注释,以说明不同的位置或符号。对于此类文档的光学字符识别,需要从文档中提取和识别单个文本行。由于这种非结构化布局中存在多方向字符,因此单词识别是一项具有挑战性的任务。在本文中,我们提出了一种使用隐马尔可夫模型(HMM)来识别图形文档中尺度和方向不变的文本词的方法。首先,采用线条提取方法对文本线条进行分割,该方法基于文本成分的前景和背景信息。为了有效地利用背景信息,这里使用了水库的概念。对于弯曲文本线的识别,估计滑动窗口的路径,并从滑动窗口中提取特征输入HMM系统进行识别。HMM采用基于局部梯度直方图(LGH)的逐帧特征。在图形词数据集上对实验结果进行了评估,取得了令人鼓舞的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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