Text detection in video images using adaptive edge detection and stroke width verificationHaojin Yang, Bernhard Quehl, Hara...
Agenda                             (1)  Introduction / Motivation                             (2)  Related works          ...
Project Mediaglobe     • Semantic Search Engine for Media Archives     •  Enable exploratory and semantic search in       ...
3        Seach in multimedia archive?!    Jörg Waitelonis, HPI | THESEUS | Innovationszentrum | 28.-29.11.2012
Automated Audiovisual Analysis!                                                                 Concept "                 ...
Common OCR vs. Video OCR•  optimized for Scans                •  low resolution•  high resolution                   •  het...
Related Works6   Most of proposed text detection methods take use of texture features, edges, colors    and some text repr...
Text Detector7    Workflow of edge based text detector:       (a) Original image             (b) Vertical edge map         ...
Text Verification – Workflow8                         e.g               Hasso Plattner Institute | H-J. Yang, B.Quehl, H. ...
SWT Based Text Verification9    Stroke Width Transformation         (a)   Boundary detection        (b)   From each bounda...
SWT Based Text Verification10     Stroke Width Transformation result example                         An example output ima...
SWT Based Text Verification11     SWT Verification Constrains:      A text candidate component is discarded if:            ...
SWT Based Text Verification12   Edge detection projection profiling                                →                      ...
Evaluation and Experimental     Results13     Experiment setup:        Test set:        •  Mediaglobe test set (31 images)...
Evaluation Results15   •  Evaluation Microsoft common test set                                         Method             ...
Conclusion16             We have presented a localization-verification                scheme for text detection in video i...
Reference17      [1] B. Epshtein, E. Ofek, Y. Wexler. “Detecting Text in Natural Scene with Stroke Width Transform,” in   ...
Text detection in video images using                           adaptive edge detection and                           strok...
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Text detection in video images using adaptive
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  1. 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. 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. 3. Project Mediaglobe • Semantic Search Engine for Media Archives •  Enable exploratory and semantic search in Audiovisual Media Archives http://www.projekt-mediaglobe.de/
  4. 4. 3 Seach in multimedia archive?! Jörg Waitelonis, HPI | THESEUS | Innovationszentrum | 28.-29.11.2012
  5. 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. 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. 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. 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. 9. Text Verification – Workflow8 e.g Hasso Plattner Institute | H-J. Yang, B.Quehl, H. Sack
  10. 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. 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. 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. 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. 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. 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. 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. 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. 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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