傅里叶统计特征在场景文本检测中的应用

H. C. Vinod, S. Niranjan, V. N. Manjunath Aradhya
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引用次数: 5

摘要

出现在图像中的文本包含重要和有用的数据。图像中的文本检测和提取已经在很多领域得到了应用。本文提出了RGB空间中的n个傅里叶统计特征和用于检测和提取相机图像中的文本的数理统计方法。在RGB空间中,傅里叶统计特征用于检测图像中复杂背景、对比字体、鲜明字体和不同字体大小的文本,在RGB空间中,基于傅里叶变换的特征与统计特征相结合,然后从RGB波段中计算出傅里叶统计特征,对图像背景中的文本像素进行模糊c均值聚类。通过检测文本块的投影轮廓,采用数理统计方法确定文本块的分类文本像素,并从图像中提取文本部分。通过对低对比度、复杂背景、多语言、对比字体和图像中文本大小的图像进行实验来检验所建议的方法。
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An application of Fourier statistical features in scene text detection
Text that appears in images contains important and useful data. Text detection and extraction in images have been applied in many applications. In this paper, we propose n Fourier-Statistical Features in RGB space and Mathematical statistical method for detecting and extracting text in camera images. In RGB space Fourier-Statistical Features is used for detecting text in the image of complex background, contrasting fonts, distinct scripts and different font sizes, In RGB space Fourier transform based features with statistical features and then figured out Fourier-Statistical Features from RGB bands are subject to Fuzzy C-means clustering to classify text pixels from the image background. Classified text pixels of text blocks are determined by inspecting the projection profiles, mathematical statistical method and extract the text part from the image. The suggested approach is examined by carrying on experiments on images of low contrast, complex background, multilingual languages, contrasting fonts, and sizes of text in the image.
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