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Layout and Perspective Distortion Independent Recognition of Captured Chinese Document Image

机译:捕获中文文档图像的布局和透视畸变独立识别

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This paper introduced a layout and perspective distortion independent recognition framework for captured Chinese document image. Under the framework, 1) Conditional random field (CRF) is employed for text line extraction from a global point of view. As the text line extraction is layout independent it could be widely used in different type of document images 2) A text line image based perspective distortion correction method is detailed and used in three different ways. 3) The text line extraction and perspective distortion correction are combined with character recognition to construct a recognition system. On three captured document image datasets, the proposed framework improves the accuracies from 94.03% to 95.20%, 13.01% to 93.71% and 10.63% to 92.68% respectively for different distortion degrees. The experimental results demonstrate that the introduced recognition framework is promising for solving layout and perspective distortion problems in captured document image recognition.
机译:本文介绍了一种用于捕获的中文文档图像的布局和透视失真独立识别框架。在该框架下,1)从全局的角度出发,将条件随机字段(CRF)用于文本行提取。由于文本行提取与布局无关,因此可以广泛用于不同类型的文档图像中。2)详细介绍了一种基于文本行图像的透视失真校正方法,并以三种不同方式使用了该方法。 3)文本行提取和透视畸变校正与字符识别相结合,构成一个识别系统。在三个捕获的文档图像数据集上,提出的框架针对不同的失真度分别将精度从94.03 \%提高到95.20 \%,将13.01 \%提高到93.71 \%,将10.63 \%提高到92.68 \%。实验结果表明,引入的识别框架有望解决捕获文档图像识别中的布局和透视失真问题。

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