Scene Text Extraction Using Focus of Mobile Camera Egyul Kim, SeongHun Lee, JinHyung Kim Artificial Intelligence & Pattern Recognition Lab, KAIST, Korea Ming Chuang University Date: Location: th International Conference on Document Analysis and Recognition 文件分析與辨識 國際研討會 S403-1

Outline 1.Introduction 2.Scene text extraction method 2.1 Selection of text color candidates 2.2 Extraction of connected components 2.3 Text verification 3.Experimental result 4.Conclusion /09 $25.00 © 2009 IEEE DOI /ICDAR

1.Introduction : The Context 3

The challenging 4  Non-uniform illumination  Lighting condition, shadows  Complex background  Outdoor images  Complex color  Complex layout  Non-text pixels surround text pixels  Window bars → ‘I’ (text-like)

Related work 5  Past …  Sobel edge detection  Otsu binarization  Connected component extraction  Rule-based connected component filtering  Binarization to gray and gray inverse  Color component  Uniform background  Complex background → missing, false  Sol : interactive graph user interface

2. Scene text extraction method 6

2.1 Selection of text color candidates 1 7  HCL (Hue, chroma, luminance)  Hue is Robust on illumination  compare to luminance or RGB color  Color difference between text : background D HCL = A L (l − l s ) 2 + A CH {c 2 + c 2 s − 2cc s cos(h − h s )} (1) √ ___________________________________________________ Seed color RGBHCL

 Mean-Shift algorithm  Non-parametric clustering  2~5 seed color  Text boundary:  Affect true text color Text & background combine Text segment into small pieces  Sobel edge 2.1 Selection of text color candidates 2 8 seed’ j Figure 3. Color distributions of pixel samples

2.2 Extraction of connected components 1 9  Binarization method  Base on HCL color space Figure 4. Components expansion 0:1

Connected component ? 10

2.2 Extraction of connected components 2 11  Adaptive binarization  Local vs. Global threshold  Text candidate  Fully connected component in binarization  Initial component Figure 5. Binarization result: (a)original image (b)HCL distance on seed color (c)Global binarization result (d)adaptive binarization result

2.3 Text verification 12  Character features  Horizontal  Geometric restrictions(height, width, compactness)  Final text reject reasons (1) Number of components ≥ 3 (2) Variation of distance between components (3) Variation of heights of components (4) Variation of compactness of components Figure 6. Text verification

4. Conclusion 1.Text color candidate  Pixel sampling  Mean-shift alg. 2.Connected components  HCL distance  Adaptive Binarization 3.Text Verification  True text components 13 PrecisionRecall Proposed method (0.89) Figure 7. Example of text detection results

Senior citizen reading assisting 14

Q & A 報告完畢 感謝聆聽 15