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Text detection in images based on unsupervised classification of edge-based features

Explore a method for detecting text in images based on unsupervised edge features classification. The method utilizes Sobel edge detection, K-mean clustering, and feature computation. Experimental results exhibit high precision in distinguishing text regions from non-text regions, robustness to varying font sizes and colors, and diverse backgrounds and languages.

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Text detection in images based on unsupervised classification of edge-based features

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  1. Text detection in images based on unsupervised classification of edge-based features • Source: International Conference on Document Analysis and Recognition, 2006 • Authors: Chunmei Liu, Chunheng Wang and Ruwei Dai • Speaker: Chih-Hao Chen • Date: 2008/06/04

  2. Introduction Original Image Result

  3. Proposed Method(1/5) Original Image Edge Image Sobel Result Compute Features K-mean

  4. Proposed Method(2/5) Sobel edge detectors: 0° 45° 90° 135°

  5. Proposed Method(3/5) 0° 45° Edge Image (Average) 90° 135°

  6. Proposed Method(4/5) Normalized in the range from 0 to 1 mean energy w h standard deviation Edge Image inertia local homogeneity

  7. Proposed Method(5/5) K-mean: Background Block Text

  8. Experimental Results(1/2) (a) Webpage image (b) Video frame (c) Magazine cover (d) Magazine cover

  9. Experimental Results(2/2) Precision with different block size w x h: 78.3% 100 images

  10. Conclusions • Effective for the distinction between text regions and non-text regions. • Robust for font-size, font-color, background complexity, and language.

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