An Improved Scene Text Extraction Method Using Conditional Random Field and Optical Character Recognition
An Improved Scene Text Extraction Method Using Conditional Random Field and Optical Character Recognition
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DOI：
10.1109/icdar.2011.148
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发表时间：
2011-09
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影响因子：
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通讯作者：
Hongwei Zhang;Changsong Liu;Cheng Yang;Xiaoqing Ding;Kongqiao Wang
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文献类型：
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作者：
Hongwei Zhang;Changsong Liu;Cheng Yang;Xiaoqing Ding;Kongqiao Wang
近年来，场景文本提取的研究发展迅速。最近，条件随机场（CRF）已被用来给连接组件（CC）的“文本”或“非文本”的标签。然而，CRF模型中的一个亟待解决的问题是多文本行提取。在本文中，我们提出了一个两步迭代CRF算法的信念传播推理和OCR过滤阶段。两种邻域关系图分别用于提取多行文本的迭代过程。此外，OCR置信度被用作识别文本区域的指标，而传统的OCR过滤模块只考虑识别结果。第一次CRF迭代旨在找到特定的文本CC，特别是在多个文本行中，并将不确定的CC发送到第二次迭代。第二次迭代为不确定CC提供第二次机会，并在OCR的帮助下过滤虚警CC。在ICDAR 2005公开数据集上的实验表明，该方法与现有算法相比具有较好的性能。
Over the past few years, research on scene text extraction has developed rapidly. Recently, condition random field (CRF) has been used to give connected components (CCs) 'text' or 'non-text' labels. However, a burning issue in CRF model comes from multiple text lines extraction. In this paper, we propose a two-step iterative CRF algorithm with a Belief Propagation inference and an OCR filtering stage. Two kinds of neighborhood relationship graph are used in the respective iterations for extracting multiple text lines. Furthermore, OCR confidence is used as an indicator for identifying the text regions, while a traditional OCR filter module only considered the recognition results. The first CRF iteration aims at finding certain text CCs, especially in multiple text lines, and sending uncertain CCs to the second iteration. The second iteration gives second chance for the uncertain CCs and filter false alarm CCs with the help of OCR. Experiments based on the public dataset of ICDAR 2005 prove that the proposed method is comparative with the existing algorithms.