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Presentation iwssip2012

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Text detection in video images using adaptive …

Text detection in video images using adaptive
edge detection and stroke width verification

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  • 1. Text detection in video images using adaptive edge detection and stroke width verificationHaojin Yang, Bernhard Quehl, Harald Sack April 11 – 13, IWSSIP2012, Vienna (Austria) Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 2. Agenda (1)  Introduction / Motivation (2)  Related works (3)  Text detection in video frames (4)  Evaluation and experimental results (5)  ConclusionJörg Waitelonis, HPI | THESEUS | Innovationszentrum | 28.-29.11.2012
  • 3. Project Mediaglobe • Semantic Search Engine for Media Archives •  Enable exploratory and semantic search in Audiovisual Media Archives http://www.projekt-mediaglobe.de/
  • 4. 3 Seach in multimedia archive?! Jörg Waitelonis, HPI | THESEUS | Innovationszentrum | 28.-29.11.2012
  • 5. Automated Audiovisual Analysis! Concept " Analysis Classification:" Studio" Indoor" News Show Logo " Overlay " Face " Text Detection Detection Scene" Audio-Mining TextStructural" Automated" Speaker" Speech" Analysis Recognition Identification Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 6. Common OCR vs. Video OCR•  optimized for Scans •  low resolution•  high resolution •  heterogeneous background•  usually white on black •  (motion) blurring •  homogenous background •  perspective distortion •  uneven illumination •  shading, rotation •  large amounts of data (Images) Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 7. Related Works6 Most of proposed text detection methods take use of texture features, edges, colors and some text representative features e.g., stroke width feature. Chen et al.[4] Text detection and recognition in images and video frames: •  edge based approaches achive a high recall rate •  but may also produce many false alarms Epshtein et al. [1] proposed the SWT (Stroke Width Transform) for text detection of nature scene images. Shortcomings of the original SWT approach: •  Robust to distinguish text like non-text objects • The computation of SWT quite costly for images with complex contents. Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 8. Text Detector7 Workflow of edge based text detector: (a) Original image (b) Vertical edge map (c) Vertical dilation map (d) Binary map of (c) (e) Binary map after (f) After projection- (g) Detection result connected profiling refinement Componet analysis Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 9. Text Verification – Workflow8 e.g Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 10. SWT Based Text Verification9 Stroke Width Transformation (a) Boundary detection (b) From each boundary pixel p send a ray along the text gradient direction, this leads to find another boundary pixel q. (c) Calculate the potential stroke width value between p und q (a) (b) (c) Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 11. SWT Based Text Verification10 Stroke Width Transformation result example An example output image from stroke width transform for character w. Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 12. SWT Based Text Verification11 SWT Verification Constrains: A text candidate component is discarded if: •  Its stroke width variance is lying inbetween (MinVar, MaxVar) threshold •  Its mean stroke width is lying inbetween (MinStroke, MaxStroke) threshold •  Generating of the character component by merging candidate components with similar stroke width value. •  Then, creating character chains by merging character components with a similar color and a small distance. •  The final verified text line must have more than 2 character chains. Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 13. SWT Based Text Verification12 Edge detection projection profiling → → SWT Text Verification on profiling candidates → → Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 14. Evaluation and Experimental Results13 Experiment setup: Test set: •  Mediaglobe test set (31 images) •  German TV news test set (72 images) •  Microsoft common test set (45 images) Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 15. Evaluation Results15 •  Evaluation Microsoft common test set Method Recall Precision F1 measure Zhao et al.[10] 0.94 0.98 0.96 Thillou et. Al [11] 0.91 0.94 0.92 Lienhard et. al.[12] 0.91 0.94 0.92 Shivakumara et. al. [4] 0.92 0.90 0.91 Gllavata et. al. [13] 0.90 0.87 0.88 0.93 0.94 0.93 Our •  Evaluation other test sets Testset Recall Precision F1 measure TV News 0.86 0.81 0.83 Mediaglobe 0.75 0.81 0.77 •  Example images: http://yovisto.com/labs/VideoOCR/visualResult/ Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 16. Conclusion16 We have presented a localization-verification scheme for text detection in video images. •  Using fast edge text detector and an adaptive refinement to reduce the false alarms •  The proposed method is quite competitive to other existing methods •  Detect differenced writing systems (English, Japanese, Arabic ) Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 17. Reference17 [1] B. Epshtein, E. Ofek, Y. Wexler. “Detecting Text in Natural Scene with Stroke Width Transform,” in Proc. of Computer Vision and Pattern Recognition, 2010, pp. 2963–2970. [2] Y. Zhong, H-J. Zhang, and A. Jain, “Automatic caption localization in compressed video,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 385– 392, 2000 [3] X. Qian, G. Liu, H. Wang, and R. Su, “Text detection, localization and tracking in compressed video,” in Proc. of Signal Processing: Image Communication, 2007, pp. 752–768 [4] Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8 (6), 679–698 (1986). DOI 10.1109/TPAMI.1986.4767851. URL http: //dx.doi.org/10.1109/TPAMI. 1986.4767851 [5] http://yovisto.com/labs/VideoOCR/ [6] http://www.cs.cityu.edu.hk/~liuwy/PE_VTDetect/ Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  • 18. Text detection in video images using adaptive edge detection and stroke width verification Thank you for your attention! Bernhard Quehl Hasso-Plattner-Institut Potsdam Prof.-Dr.-Helmert Str. 2-4 14482 Potsdam phone:  #+49 (0)331-5509-548# email: bernhard.quehl@hpi.uni-potsdam.de# web:   http://www.hpi.uni-potsdam.de/#Jörg Waitelonis, HPI | THESEUS | Innovationszentrum | 28.-29.11.2012

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