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首页> 外文会议>International conference on graphic and image processing >Detecting Text in Natural Scenes with Multi-level MSER and SWT
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Detecting Text in Natural Scenes with Multi-level MSER and SWT

机译：使用多级MSER和SWT在自然场景中检测文本

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The detection of the characters in the natural scene is susceptible to factors such as complex background, variable viewing angle and diverse forms of language, which leads to poor detection results. Aiming at these problems, a new text detection method was proposed, which consisted of two main stages, candidate region extraction and text region detection. At first stage, the method used multiple scale transformations of original image and multiple thresholds of maximally stable extremal regions (MSER) to detect the text regions which could detect character regions comprehensively. At second stage, obtained SWT maps by using the stroke width transform (SWT) algorithm to compute the candidate regions, then using cascaded classifiers to propose non-text regions. The proposed method was evaluated on the standard benchmark datasets of ICDAR2011 and the datasets that we made our own data sets. The experiment results showed that the proposed method have greatly improved that compared to other text detection methods.
机译：自然场景中人物的检测容易受到诸如背景复杂，视角可变和语言形式多样等因素的影响，从而导致检测结果较差。针对这些问题，提出了一种新的文本检测方法，该方法包括两个主要阶段：候选区域提取和文本区域检测。在第一阶段，该方法使用原始图像的多尺度转换和最大稳定的极值区域（MSER）的多个阈值来检测可以全面检测字符区域的文本区域。在第二阶段，通过使用笔划宽度变换（SWT）算法计算候选区域，然后使用级联分类器提出非文本区域，从而获得SWT映射。在ICDAR2011的标准基准数据集和我们制作自己的数据集的数据集上对提出的方法进行了评估。实验结果表明，与其他文本检测方法相比，该方法具有很大的改进。

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