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Detection of comic characters in comic books 
and manga 
17/10/14 - christophe-rigaud.com
2 
17/10/14 - christophe-rigaud.com 
Presentation 
Advisers 
● Jean-Christophe Burie & Jean-Marc Ogier (L3i, La Rochelle, France) 
● Dimosthenis Karatzas (CVC, Barcelona, Spain) 
eBDtheque project 
● Comic book image processing and understanding 
● Cultural heritage in many countries 
● Paper ↔ digital comic book conversion enhancement 
● New usages using the new technologies 
Applications 
● Text/image search, augmented reading, reflowing... 
France 
Spain
3 
What researchers can do for the paper to digital transition? 
Researchers 
Paper world 
Digital world Digital world 
17/10/14 - christophe-rigaud.com
4 
Researchers 
Image credits: contributors of the eBDtheque dataset 
17/10/14 - christophe-rigaud.com
5 
17/10/14 - christophe-rigaud.com 
Balloon classification 
Speaker localization 
Text timestamps 
Tone and pitch 
Scene understanding 
Layout analysis 
Panel retrieval 
Reflowing 
De-hyphenation 
Speech synthesis 
Language analysis 
Translation assistance 
Pose retrieval 
Facial expression 
Relationship retrieval 
Character localization and recognition 
Researchers 
PPAANNEELLSS
6 
17/10/14 - christophe-rigaud.com 
Balloon classification 
Speaker localization 
Text timestamps 
Tone and pitch 
Scene understanding 
Layout analysis 
Panel retrieval 
Reflowing 
De-hyphenation 
Speech synthesis 
Language analysis 
Translation assistance 
Pose retrieval 
Facial expression 
Relationship retrieval 
Character localization and recognition 
Researchers 
TTEEXXTT
7 
17/10/14 - christophe-rigaud.com 
Balloon classification 
Speaker localization 
Text timestamps 
Tone and pitch 
Scene understanding 
Layout analysis 
Panel retrieval 
Reflowing 
De-hyphenation 
Speech synthesis 
Language analysis 
Translation assistance 
Pose retrieval 
Facial expression 
Relationship retrieval 
Character localization and recognition 
Researchers 
BBAALLLLOOOONNSS
8 
CCHHAARRAACCTTEERRSS 
17/10/14 - christophe-rigaud.com 
Balloon classification 
Speaker localization 
Text timestamps 
Tone and pitch 
Scene understanding 
Layout analysis 
Panel retrieval 
Reflowing 
De-hyphenation 
Speech synthesis 
Language analysis 
Translation assistance 
Pose retrieval 
Facial expression 
Relationship retrieval 
Character localization and recognition 
Researchers
9 
Challenges of comics analysis 
● Variation of comic book styles 
● Printing & scanning method changes over time (press, ink-jet, computer-based) 
● Diversity of information to extract 
● A lot of semantic relations to retrieve (element's relationships, text 
17/10/14 - christophe-rigaud.com 
meaning)
11 
Summary of comic character extraction 
17/10/14 - christophe-rigaud.com 
● Supervised approaches 
● Character spotting given an example 
● Unsupervised approaches 
● Hypothesis from speech balloon tail position and orientation 
● Region of interest from simple object removal 
● Graph­based 
approach (Nam)
12 
Summary of comic character extraction 
17/10/14 - christophe-rigaud.com 
● Supervised approaches 
● Character spotting given an example 
● Unsupervised approaches 
● Hypothesis from speech balloon tail position and orientation 
● Region of interest from simple object removal 
● Graph­based 
approach (Nam)
13 
Character spotting given an example [5] 
● Aim: retrieve all the instances (apparitions) of a given character in all the 
pages of a comic book title/album/collection 
17/10/14 - christophe-rigaud.com 
● Method summary: 
1) Reduction of the number of colors 
2) Get the colors of the query example (user interaction) 
3) Compute the user query descriptor 
4) Spot similar character instances 
[5] C. Rigaud, D. Karatzas, J-C Burie, J-M Ogier Color descriptor for content-based drawing retrieval DAS'14
14 
1) Color reduction/quantization 
N>10000 N=256 N=32 
17/10/14 - christophe-rigaud.com
15 
17/10/14 - christophe-rigaud.com 
2) User query
16 
3) Color selection: occurrence level 
17/10/14 - christophe-rigaud.com
17 
3) Color selection: occurrence + discriminality levels 
17/10/14 - christophe-rigaud.com 
Top 3 colors
18 
4) Character spotting: example 
17/10/14 - christophe-rigaud.com 
User input 
Image credits: (Prunelle, la fille du cyclope, Ankama, 2010 
Descriptor
19 
4) Character spotting: example 
correct detections wrong detections 
17/10/14 - christophe-rigaud.com
20 
17/10/14 - christophe-rigaud.com 
Conclusions 
● Contributions 
● Content Based Drawing Retrieval 
● Color descriptor robust to scale, 
rotation, translation, deformation and 
occlusion 
● Benefit of redundant information 
● Limitations 
● Colored comics 
● Number of color (quantization) 
● Requires one example of each character 
Query Correct Wrong 
Result examples
21 
Summary of comic character extraction 
17/10/14 - christophe-rigaud.com 
● Supervised approaches 
● Character spotting given an example 
● Unsupervised approaches 
● Hypothesis from speech balloon tail position and orientation 
● Region of interest from simple object removal 
● Graph­based 
approach (Nam)
22 
Hypothesis from speech balloon tail position and direction 
● Given balloons in a panel, where are the (speaking) characters? 
17/10/14 - christophe-rigaud.com
23 
17/10/14 - christophe-rigaud.com 
Example
24 
17/10/14 - christophe-rigaud.com 
Example
25 
17/10/14 - christophe-rigaud.com 
Conclusions 
● Preliminary results 
● Highly relies on the quality of the 
tail detection 
● Only for “speaking” characters 
(others can be spotted) 
● Implicitly retrieves the 
relationship between balloon and 
character
26 
Summary of comic character extraction 
17/10/14 - christophe-rigaud.com 
● Supervised approaches 
● Character spotting given an example 
● Unsupervised approaches 
● Hypothesis from speech balloon tail position and orientation 
● Region of interest from simple object removal 
● Graph­based 
approach (Nam)
27 
Region of interest (ROI) from simple object removal 
● Given an image from a double page of manga, can you predict where the 
17/10/14 - christophe-rigaud.com 
characters are?
28 
Region of interest (ROI) from simple object removal 
● Given an image from a double page of manga, can you predict where the 
? ? 
17/10/14 - christophe-rigaud.com 
characters are? 
? ? 
? ? 
? 
? ? 
? 
? ?
29 
Region of interest (ROI) from simple object removal 
● Given an image from a double page of manga, can you predict where the 
17/10/14 - christophe-rigaud.com 
characters are? 
? ? 
? ? 
? 
? ? 
? 
42% 
correct 
? ? ? ? 
Image credits: Yasuhisa Hara, "Kingdom", Shueisha, since 2006.
30 
Region of interest (ROI) from simple object removal 
● In the same image, knowing the position of panels and balloons, can you 
predict where the comic characters are? 
17/10/14 - christophe-rigaud.com
31 
Region of interest (ROI) from simple object removal 
● Knowing the position of panels and balloons, can you predict where the 
? 
17/10/14 - christophe-rigaud.com 
comic characters are? 
? ? 
? 
? 
? 
? 
? 
??? 
?
32 
Region of interest (ROI) from simple object removal 
? 
17/10/14 - christophe-rigaud.com 
● Et voilà ! 
? ? 
? 
? 
? 
? 
? 
?? 
? ? 83% 
correct
33 
A first test from automatic panels and balloons 
extraction... 
17/10/14 - christophe-rigaud.com
34 
Original image 
17/10/14 - christophe-rigaud.com
35 
Image masked 
17/10/14 - christophe-rigaud.com
36 
Image masked + distance transform 
17/10/14 - christophe-rigaud.com
39 
17/10/14 - christophe-rigaud.com 
To be continued...
40 
17/10/14 - christophe-rigaud.com 
Graph-based approach 
● Presented by Nam Le Thanh
41 
Live demo: real time panels and balloons extraction 
17/10/14 - christophe-rigaud.com
42 
https://github.com/crigaud/publication/tree/master/2014/TALK/intelligent_media_processing_group 
17/10/14 - christophe-rigaud.com

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Detection of comic characters in comic books and manga

  • 1. Detection of comic characters in comic books and manga 17/10/14 - christophe-rigaud.com
  • 2. 2 17/10/14 - christophe-rigaud.com Presentation Advisers ● Jean-Christophe Burie & Jean-Marc Ogier (L3i, La Rochelle, France) ● Dimosthenis Karatzas (CVC, Barcelona, Spain) eBDtheque project ● Comic book image processing and understanding ● Cultural heritage in many countries ● Paper ↔ digital comic book conversion enhancement ● New usages using the new technologies Applications ● Text/image search, augmented reading, reflowing... France Spain
  • 3. 3 What researchers can do for the paper to digital transition? Researchers Paper world Digital world Digital world 17/10/14 - christophe-rigaud.com
  • 4. 4 Researchers Image credits: contributors of the eBDtheque dataset 17/10/14 - christophe-rigaud.com
  • 5. 5 17/10/14 - christophe-rigaud.com Balloon classification Speaker localization Text timestamps Tone and pitch Scene understanding Layout analysis Panel retrieval Reflowing De-hyphenation Speech synthesis Language analysis Translation assistance Pose retrieval Facial expression Relationship retrieval Character localization and recognition Researchers PPAANNEELLSS
  • 6. 6 17/10/14 - christophe-rigaud.com Balloon classification Speaker localization Text timestamps Tone and pitch Scene understanding Layout analysis Panel retrieval Reflowing De-hyphenation Speech synthesis Language analysis Translation assistance Pose retrieval Facial expression Relationship retrieval Character localization and recognition Researchers TTEEXXTT
  • 7. 7 17/10/14 - christophe-rigaud.com Balloon classification Speaker localization Text timestamps Tone and pitch Scene understanding Layout analysis Panel retrieval Reflowing De-hyphenation Speech synthesis Language analysis Translation assistance Pose retrieval Facial expression Relationship retrieval Character localization and recognition Researchers BBAALLLLOOOONNSS
  • 8. 8 CCHHAARRAACCTTEERRSS 17/10/14 - christophe-rigaud.com Balloon classification Speaker localization Text timestamps Tone and pitch Scene understanding Layout analysis Panel retrieval Reflowing De-hyphenation Speech synthesis Language analysis Translation assistance Pose retrieval Facial expression Relationship retrieval Character localization and recognition Researchers
  • 9. 9 Challenges of comics analysis ● Variation of comic book styles ● Printing & scanning method changes over time (press, ink-jet, computer-based) ● Diversity of information to extract ● A lot of semantic relations to retrieve (element's relationships, text 17/10/14 - christophe-rigaud.com meaning)
  • 10. 11 Summary of comic character extraction 17/10/14 - christophe-rigaud.com ● Supervised approaches ● Character spotting given an example ● Unsupervised approaches ● Hypothesis from speech balloon tail position and orientation ● Region of interest from simple object removal ● Graph­based approach (Nam)
  • 11. 12 Summary of comic character extraction 17/10/14 - christophe-rigaud.com ● Supervised approaches ● Character spotting given an example ● Unsupervised approaches ● Hypothesis from speech balloon tail position and orientation ● Region of interest from simple object removal ● Graph­based approach (Nam)
  • 12. 13 Character spotting given an example [5] ● Aim: retrieve all the instances (apparitions) of a given character in all the pages of a comic book title/album/collection 17/10/14 - christophe-rigaud.com ● Method summary: 1) Reduction of the number of colors 2) Get the colors of the query example (user interaction) 3) Compute the user query descriptor 4) Spot similar character instances [5] C. Rigaud, D. Karatzas, J-C Burie, J-M Ogier Color descriptor for content-based drawing retrieval DAS'14
  • 13. 14 1) Color reduction/quantization N>10000 N=256 N=32 17/10/14 - christophe-rigaud.com
  • 14. 15 17/10/14 - christophe-rigaud.com 2) User query
  • 15. 16 3) Color selection: occurrence level 17/10/14 - christophe-rigaud.com
  • 16. 17 3) Color selection: occurrence + discriminality levels 17/10/14 - christophe-rigaud.com Top 3 colors
  • 17. 18 4) Character spotting: example 17/10/14 - christophe-rigaud.com User input Image credits: (Prunelle, la fille du cyclope, Ankama, 2010 Descriptor
  • 18. 19 4) Character spotting: example correct detections wrong detections 17/10/14 - christophe-rigaud.com
  • 19. 20 17/10/14 - christophe-rigaud.com Conclusions ● Contributions ● Content Based Drawing Retrieval ● Color descriptor robust to scale, rotation, translation, deformation and occlusion ● Benefit of redundant information ● Limitations ● Colored comics ● Number of color (quantization) ● Requires one example of each character Query Correct Wrong Result examples
  • 20. 21 Summary of comic character extraction 17/10/14 - christophe-rigaud.com ● Supervised approaches ● Character spotting given an example ● Unsupervised approaches ● Hypothesis from speech balloon tail position and orientation ● Region of interest from simple object removal ● Graph­based approach (Nam)
  • 21. 22 Hypothesis from speech balloon tail position and direction ● Given balloons in a panel, where are the (speaking) characters? 17/10/14 - christophe-rigaud.com
  • 22. 23 17/10/14 - christophe-rigaud.com Example
  • 23. 24 17/10/14 - christophe-rigaud.com Example
  • 24. 25 17/10/14 - christophe-rigaud.com Conclusions ● Preliminary results ● Highly relies on the quality of the tail detection ● Only for “speaking” characters (others can be spotted) ● Implicitly retrieves the relationship between balloon and character
  • 25. 26 Summary of comic character extraction 17/10/14 - christophe-rigaud.com ● Supervised approaches ● Character spotting given an example ● Unsupervised approaches ● Hypothesis from speech balloon tail position and orientation ● Region of interest from simple object removal ● Graph­based approach (Nam)
  • 26. 27 Region of interest (ROI) from simple object removal ● Given an image from a double page of manga, can you predict where the 17/10/14 - christophe-rigaud.com characters are?
  • 27. 28 Region of interest (ROI) from simple object removal ● Given an image from a double page of manga, can you predict where the ? ? 17/10/14 - christophe-rigaud.com characters are? ? ? ? ? ? ? ? ? ? ?
  • 28. 29 Region of interest (ROI) from simple object removal ● Given an image from a double page of manga, can you predict where the 17/10/14 - christophe-rigaud.com characters are? ? ? ? ? ? ? ? ? 42% correct ? ? ? ? Image credits: Yasuhisa Hara, "Kingdom", Shueisha, since 2006.
  • 29. 30 Region of interest (ROI) from simple object removal ● In the same image, knowing the position of panels and balloons, can you predict where the comic characters are? 17/10/14 - christophe-rigaud.com
  • 30. 31 Region of interest (ROI) from simple object removal ● Knowing the position of panels and balloons, can you predict where the ? 17/10/14 - christophe-rigaud.com comic characters are? ? ? ? ? ? ? ? ??? ?
  • 31. 32 Region of interest (ROI) from simple object removal ? 17/10/14 - christophe-rigaud.com ● Et voilà ! ? ? ? ? ? ? ? ?? ? ? 83% correct
  • 32. 33 A first test from automatic panels and balloons extraction... 17/10/14 - christophe-rigaud.com
  • 33. 34 Original image 17/10/14 - christophe-rigaud.com
  • 34. 35 Image masked 17/10/14 - christophe-rigaud.com
  • 35. 36 Image masked + distance transform 17/10/14 - christophe-rigaud.com
  • 36. 39 17/10/14 - christophe-rigaud.com To be continued...
  • 37. 40 17/10/14 - christophe-rigaud.com Graph-based approach ● Presented by Nam Le Thanh
  • 38. 41 Live demo: real time panels and balloons extraction 17/10/14 - christophe-rigaud.com