Text detection in images based on unsupervised classification of high-frequency wavelet coefficients

被引：102

作者：

Gllavata, J ^{[1
]}

Ewerth, R ^{[1
]}

Freisleben, B ^{[1
]}

机构：

[1] Univ Siegen, SFB, FK 615, D-57068 Siegen, Germany

来源：

PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 1 | 2004年

关键词：

D O I：

10.1109/ICPR.2004.1334146

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Text localization and recognition in images is important for searching information in digital photo archives, video databases and web sites. However, since text is often printed against a complex background, it is often difficult to detect. In this paper, a robust text localization approach is presented, which can automatically detect horizontally aligned text with different sizes, fonts, colors and languages. First, a wavelet transform is applied to the image and the distribution of high-frequency wavelet coefficients is considered to statistically characterize text and non-text areas. Then, the k-means algorithm is used to classify text areas in the image. The detected text areas undergo a projection analysis in order to refine their localization. Finally, a binary segmented text image is generated, to be used as input to an OCR engine. The detection performance of our approach is demonstrated by presenting experimental results for a set of video frames taken from the MPEG-7 video test set.

引用

页码：425 / 428

页数：4

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