使用图像智能注释的文本图像分类器

N. Chiba
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引用次数: 0

摘要

提出了一种只需要图像注释的文本图像分类器。虽然已经研究了使用分类器的文本检测方法,但它们需要人工操作员进行逐字符注释,这是构建文本检测系统时最耗时的阶段。无论图像是否包含文本,所提出的分类器都使用图像智能注释,这比字符智能注释需要的操作要少得多。从这个注释中,分类器估计在图像中检测文本字符候选的可能性,以及系统根据先验概率确定图像是否包含文本的阈值。使用真实图像的实验证明了本文提出的文本图像分类器的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Text Image Classifier Using Image-Wise Annotation
A text image classifier that requires only image-wise annotation is proposed. Although text detection methods using classifiers have been investigated, they require character-wise annotation by human operators, which is the most time-consuming phase when constructing a text detection system. The proposed classifier uses image-wise annotation whether the image contains text or not, which requires much less effort by an operator than that of character-wise annotation. From this annotation, the classifier estimates likelihood of detecting text-character candidates in an image as well as the threshold value for the system to determine if the image contains text based on prior probabilities. Experiments using real images showed the effectiveness of the proposed text image classifier.
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