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Generative Ink: Data-Driven Computational Models for Digital Ink

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Artificial Intelligence for Human Computer Interaction: A Modern Approach

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

Digital ink promises to combine the flexibility of pen and paper interaction and the versatility of digital devices. Computational models of digital ink often focus on recognition of the content by following discriminative techniques such as classification, albeit at the cost of ignoring or losing personalized style. In this chapter, we propose augmenting the digital ink framework via generative modeling to achieve a holistic understanding of the ink content. Our focus particularly lies in developing novel generative models to gain fine-grained control by preserving user style. To this end, we model the inking process and learn to create ink samples similar to users. We first present how digital handwriting can be disentangled into style and content to implement editable digital ink, enabling content synthesis and editing. Second, we address a more complex setup of free-form sketching and propose a novel approach for modeling stroke-based data efficiently. Generative ink promises novel functionalities, leading to compelling applications to enhance the inking experience for users in an interactive and collaborative manner.

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Notes

  1. 1.

    https://quickdraw.withgoogle.com/.

  2. 2.

    https://eth-ait.github.io/cose.

  3. 3.

    https://www.youtube.com/watch?v=NVF-1csvVvc.

  4. 4.

    https://www.youtube.com/watch?v=GENck9zmpMY.

References

  1. Aksan E, Pece F, Hilliges O (2018) DeepWriting: making digital Ink editable via deep generative modeling, association for computing machinery, New York, NY, USA, pp 1–14. https://doi.org/10.1145/3173574.3173779

  2. Aksan E, Deselaers T, Tagliasacchi A, Hilliges O (2020) Cose: compositional stroke embeddings. arXiv:200609930

  3. Annett M (2017) (digitally) inking in the 21st century. IEEE Comput Graph Appl 37(1):92–99. https://doi.org/10.1109/MCG.2017.1

  4. Annett M, Anderson F, Bischof WF, Gupta A (2014) The pen is mightier: Understanding stylus behaviour while inking on tablets. In: Proceedings of graphics interface 2014, Canadian information processing society, CAN, GI ’14, pp 193–200

    Google Scholar 

  5. Arvo J, Novins K (2000) Fluid sketches: continuous recognition and morphing of simple hand-drawn shapes. In: Proceedings of the 13th annual ACM symposium on User interface software and technology. ACM, pp 73–80

    Google Scholar 

  6. Arvo J, Novins K (2005) Appearance-preserving manipulation of hand-drawn graphs. In: Proceedings of the 3rd international conference on Computer graphics and interactive techniques in Australasia and South East Asia. ACM, pp 61–68

    Google Scholar 

  7. Berninger VW (2012) Strengthening the mind’s eye: the case for continued handwriting instruction in the 21st century. Principal 91:28–31

    Google Scholar 

  8. Bhattacharya U, Plamondon R, Chowdhury SD, Goyal P, Parui SK (2017) A sigma-lognormal model-based approach to generating large synthetic online handwriting sample databases. Int J Doc Anal Recogn (IJDAR) 1–17

    Google Scholar 

  9. Bhunia AK, Ghose S, Kumar A, Chowdhury PN, Sain A, Song YZ (2021a) Metahtr: towards writer-adaptive handwritten text recognition. arXiv:210401876

  10. Bhunia AK, Khan S, Cholakkal H, Anwer RM, Khan FS, Shah M (2021b) Handwriting transformers. arXiv:210403964

  11. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press Inc, USA

    MATH  Google Scholar 

  12. Brandl P, Richter C, Haller M (2010) Nicebook: supporting natural note taking. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, NY, USA, CHI ’10, pp 599–608. https://doi.org/10.1145/1753326.1753417

  13. Bresler M, Phan TV, Průša D, Nakagawa M, Hlaváč V (2014) Recognition system for on-line sketched diagrams. In: ICFHR

    Google Scholar 

  14. Bresler M, Průša D, Hlaváč V (2016) Online recognition of sketched arrow-connected diagrams. IJDAR

    Google Scholar 

  15. Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. In: 2005 IEEE computer society conference on computer vision and pattern recognition, CVPR’05, vol 2, pp 60–65. https://doi.org/10.1109/CVPR.2005.38

  16. Burgert HJ (2002) The calligraphic line: thoughts on the art of writing. H-J Burgert, translated by Brody Neuenschwander

    Google Scholar 

  17. Carbune V, Gonnet P, Deselaers T, Rowley HA, Daryin A, Calvo M, Wang LL, Keysers D, Feuz S, Gervais P (2020) Fast multi-language LSTM-based online handwriting recognition. IJDAR

    Google Scholar 

  18. Chang WD, Shin J (2012) A statistical handwriting model for style-preserving and variable character synthesis. Int J Doc Anal Recogn 15(1):1–19. https://doi.org/10.1007/s10032-011-0147-7

  19. Chen HI, Lin TJ, Jian XF, Shen IC, Chen BY (2015) Data-driven handwriting synthesis in a conjoined manner. Comput Graph Forum 34(7):235–244. https://doi.org/10.1111/cgf.12762

  20. Cheng Y, Wang D, Zhou P, Zhang T (2017) A survey of model compression and acceleration for deep neural networks. arXiv:171009282

  21. Cherubini M, Venolia G, DeLine R, Ko AJ (2007) Let’s go to the whiteboard: how and why software developers use drawings. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, NY, USA, CHI ’07, pp 557–566. https://doi.org/10.1145/1240624.1240714

  22. Chung J, Kastner K, Dinh L, Goel K, Courville AC, Bengio Y (2015) A recurrent latent variable model for sequential data. arXiv:1506.02216

  23. Costagliola G, Deufemia V, Risi M (2006) A multi-layer parsing strategy for on-line recognition of hand-drawn diagrams. In: Visual languages and human-centric computing

    Google Scholar 

  24. Davis B, Tensmeyer C, Price B, Wigington C, Morse B, Jain R (2020) Text and style conditioned gan for generation of offline handwriting lines. arXiv:200900678

  25. Davis RC, Landay JA, Chen V, Huang J, Lee RB, Li FC, Lin J, Morrey CB III, Schleimer B, Price MN, Schilit BN (1999) Notepals: Lightweight note sharing by the group, for the group. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, NY, USA, CHI ’99, pp 338–345. https://doi.org/10.1145/302979.303107

  26. Drucker J (1995) The alphabetic labyrinth: the letters in history and imagination. Thames and Hudson

    Google Scholar 

  27. Elarian Y, Abdel-Aal R, Ahmad I, Parvez MT, Zidouri A (2014) Handwriting synthesis: classifications and techniques. Int J Doc Anal Recogn 17(4):455–469. https://doi.org/10.1007/s10032-014-0231-x

  28. Elsen C, Häggman A, Honda T, Yang MC (2012) Representation in early stage design: an analysis of the influence of sketching and prototyping in design projects. Int Des Eng Tech Conf Comput Inf Eng Conf Am Soc Mech Eng 45066:737–747

    Google Scholar 

  29. Espana-Boquera S, Castro-Bleda MJ, Gorbe-Moya J, Zamora-Martinez F (2011) Improving offline handwritten text recognition with hybrid hmm/ann models. Trans Pattern Recogn Mach Intell 33(4):767–779

    Article  Google Scholar 

  30. Evernote Corporation (2017) How evernotes image recognition works. http://blog.evernote.com/tech/2013/07/18/how-evernotes-image-recognition-works/. Accessed 10 Aug 2017

  31. Fogel S, Averbuch-Elor H, Cohen S, Mazor S, Litman R (2020) Scrabblegan: semi-supervised varying length handwritten text generation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4324–4333

    Google Scholar 

  32. Gadelha M, Wang R, Maji S (2020) Deep manifold prior

    Google Scholar 

  33. Gervais P, Deselaers T, Aksan E, Hilliges O (2020) The DIDI dataset: digital ink diagram data

    Google Scholar 

  34. Google Creative Lab (2017) Quick, draw! The data. https://quickdraw.withgoogle.com/data. Accessed 01 May 2020

  35. Graves A (2013) Generating sequences with recurrent neural networks. arXiv:1308.0850

  36. Groueix T, Fisher M, Kim V, Russell B, Aubry M (2018) Atlasnet: a papier-mâché approach to learning 3D surface generation. In: CVPR

    Google Scholar 

  37. Gurumurthy S, Sarvadevabhatla RK, Radhakrishnan VB (2017) Deligan: generative adversarial networks for diverse and limited data. arXiv:170602071

  38. Ha D, Eck D (2017) A neural representation of sketch drawings

    Google Scholar 

  39. Haines TS, Mac Aodha O, Brostow GJ (2016) My text in your handwriting. In: Transactions on graphics

    Google Scholar 

  40. Haller M, Leitner J, Seifried T, Wallace JR, Scott SD, Richter C, Brandl P, Gokcezade A, Hunter S (2010) The nice discussion room: Integrating paper and digital media to support co-located group meetings. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, NY, USA, CHI ’10, pp 609–618. https://doi.org/10.1145/1753326.1753418

  41. Hinckley K, Pahud M, Benko H, Irani P, Guimbretière F, Gavriliu M, Chen XA, Matulic F, Buxton W, Wilson A (2014) Sensing techniques for tablet+stylus interaction. In: Proceedings of the 27th annual ACM symposium on user interface software and technology. ACM, New York, NY, USA, UIST ’14, pp 605–614. https://doi.org/10.1145/2642918.2647379

  42. Hinton G, Nair V (2005) Inferring motor programs from images of handwritten digits. In: Proceedings of the 18th international conference on neural information processing systems. MIT Press, Cambridge, MA, USA, NIPS’05, pp 515–522. http://dl.acm.org/citation.cfm?id=2976248.2976313

  43. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  44. Huang CZA, Vaswani A, Uszkoreit J, Shazeer N, Simon I, Hawthorne C, Dai AM, Hoffman MD, Dinculescu M, Eck D (2018) Music transformer. arXiv:180904281

  45. Hussain F, Zalik B (1999) Towards a feature-based interactive system for intelligent font design. In: Proceedings of the 1999 IEEE international conference on information visualization, pp 378–383. https://doi.org/10.1109/IV.1999.781585

  46. Johansson S, Eric A, Roger G, Geoffrey L (1986) The tagged LOB corpus: user’s manual. Norwegian computing centre for the humanities, Bergen, Norway

    Google Scholar 

  47. Kienzle W, Hinckley K (2013) Writing handwritten messages on a small touchscreen. In: Proceedings of the 15th international conference on human-computer interaction with mobile devices and services. ACM, New York, NY, USA, MobileHCI ’13, pp 179–182. https://doi.org/10.1145/2493190.2493200

  48. Kingma DP, Welling M (2013a) Auto-encoding variational bayes. In: Proceedings of the 2nd international conference on learning representations (ICLR), 2014

    Google Scholar 

  49. Kingma DP, Welling M (2013b) Auto-encoding variational bayes

    Google Scholar 

  50. Knuth DE (1986) The metafont book. Addison-Wesley Longman Publishing Co Inc, Boston, MA, USA

    MATH  Google Scholar 

  51. Kotani A, Tellex S, Tompkin J (2020) Generating handwriting via decoupled style descriptors. In: European conference on computer vision. Springer, pp 764–780

    Google Scholar 

  52. Kumar A, Marks TK, Mou W, Feng C, Liu X (2019) UGLLI face alignment: estimating uncertainty with gaussian log-likelihood loss. In: ICCV workshops, pp 0–0

    Google Scholar 

  53. Lewis JR, Sauro J (2009) The factor structure of the system usability scale. In: Kurosu M (ed) Proceedings of the human centered design: first international conference, HCD 2009. Springer, Berlin, Heidelberg, pp 94–103. https://doi.org/10.1007/978-3-642-02806-9_12

  54. Li K, Pang K, Song YZ, Xiang T, Hospedales T, Zhang H (2019) Toward deep universal sketch perceptual grouper. Trans image processing

    Google Scholar 

  55. Li Y, Li W (2018) A survey of sketch-based image retrieval. Mach Vis Appl 29(7):1083–1100

    Article  Google Scholar 

  56. Liu G, Reda FA, Shih KJ, Wang TC, Tao A, Catanzaro B (2018) Image inpainting for irregular holes using partial convolutions. In: Proceedings of the European conference on computer vision (ECCV), pp 85–100

    Google Scholar 

  57. Liwicki M, Bunke H (2005) Iam-ondb. an on-line English sentence database acquired from handwritten text on a whiteboard. In: In Proceedings of the 8th international conference on document analysis and recognition, pp 956–961

    Google Scholar 

  58. Liwicki M, Graves A, Bunke H, Schmidhuber J (2007) A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks. In: Proceedings of the 9th international conference on document analysis and recognition, ICDAR 2007

    Google Scholar 

  59. Locatello F, Bauer S, Lucic M, Rätsch G, Gelly S, Schölkopf B, Bachem O (2018) Challenging common assumptions in the unsupervised learning of disentangled representations

    Google Scholar 

  60. Lu J, Yu F, Finkelstein A, DiVerdi S (2012) Helpinghand: example-based stroke stylization. ACM Trans Graph 31(4):46:1–46:10. https://doi.org/10.1145/2185520.2185542

  61. Maaten LVD, Hinton G (2008) Visualizing data using t-sne. JMLR 9(Nov):2579–2605

    Google Scholar 

  62. Marti UV, Bunke H (2002) The IAM-database: an English sentence database for offline handwriting recognition. IJDAR 5(1):39–46

    Article  Google Scholar 

  63. Mueller PA, Oppenheimer DM (2014) The pen is mightier than the keyboard: Advantages of longhand over laptop note taking. Psychol Sci. https://doi.org/10.1177/0956797614524581, http://pss.sagepub.com/content/early/2014/04/22/0956797614524581.abstract

  64. Mynatt ED, Igarashi T, Edwards WK, LaMarca A (1999) Flatland: new dimensions in office whiteboards. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, NY, USA, CHI ’99, pp 346–353. https://doi.org/10.1145/302979.303108

  65. MyScript (2016) MyScript: the power of handwriting. http://myscript.com/. Accessed 04 Oct 2016

  66. Noordzij G (2005) The stroke: theory of writing. Hyphen, translated from the Dutch, London

    Google Scholar 

  67. Oord A, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K (2016) Wavenet: a generative model for raw audio. arXiv:160903499

  68. Park T, Liu MY, Wang TC, Zhu JY (2019) Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 2337–2346

    Google Scholar 

  69. Perteneder F, Bresler M, Grossauer EM, Leong J, Haller M (2015) cluster: smart clustering of free-hand sketches on large interactive surfaces. In: Proceedings of the 28th annual ACM symposium on user interface software and technology. ACM, New York, NY, USA, UIST ’15, pp 37–46. https://doi.org/10.1145/2807442.2807455

  70. Pfeuffer K, Hinckley K, Pahud M, Buxton B (2017) Thumb + pen interaction on tablets. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM, New York, NY, USA, CHI ’17, pp 3254–3266. https://doi.org/10.1145/3025453.3025567

  71. Plamondon R, Srihari SN (2000) On-line and off-line handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22(1):63–84. https://doi.org/10.1109/34.824821

  72. Plamondon R, O’reilly C, Galbally J, Almaksour A, Anquetil É (2014) Recent developments in the study of rapid human movements with the kinematic theory: applications to handwriting and signature synthesis. Pattern Recogn Lett 35:225–235

    Google Scholar 

  73. Pulver MAE (1972) Symbolik der handschrift, new. Kindler, Munich

    Google Scholar 

  74. Qi CR, Su H, Mo K, Guibas LJ (2017) Pointnet: Deep learning on point sets for 3d classification and segmentation. In: CVPR, pp 652–660

    Google Scholar 

  75. Ribeiro L, Bui T, Collomosse J, Ponti M (2020) Sketchformer: transformer-based representation for sketched structure

    Google Scholar 

  76. Riche Y, Henry Riche N, Hinckley K, Panabaker S, Fuelling S, Williams S (2017) As we may ink?: Learning from everyday analog pen use to improve digital ink experiences. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM, New York, NY, USA, CHI ’17, pp 3241–3253. https://doi.org/10.1145/3025453.3025716

  77. Robinson A (2007) The story of writing. Thames & Hudson, London, UK

    Google Scholar 

  78. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  Google Scholar 

  79. Sellen AJ, Harper RH (2003) The myth of the paperless office. MIT Press, Cambridge, MA, USA

    Google Scholar 

  80. Shamir A, Rappoport A (1998) Feature-based design of fonts using constraints. In: International conference on raster imaging and digital typography. Springer, pp 93–108

    Google Scholar 

  81. Shi J, Malik J (2000) Normalized cuts and image segmentation. PAMI

    Google Scholar 

  82. Srihari S, Cha S, Arora H, Lee S (2002) Individuality of handwriting. J Forensic Sci 47(4):1–17. https://doi.org/10.1520/JFS15447J

    Article  Google Scholar 

  83. Subramonyam H, Seifert C, Shah P, Adar E (2020) texsketch: active diagramming through pen-and-ink annotations. In: Proceedings of the 2020 CHI conference on human factors in computing systems, pp 1–13

    Google Scholar 

  84. Sutherland CJ, Luxton-Reilly A, Plimmer B (2016) Freeform digital ink annotations in electronic documents: a systematic mapping study. Comput Graph 55(C):1–20. https://doi.org/10.1016/j.cag.2015.10.014

  85. Sutherland IE (1963) Sketchpad: A man-machine graphical communication system. In: Proceedings of the 21–23 May 1963, spring joint computer conference. ACM, New York, NY, USA, AFIPS ’63 (Spring), pp 329–346. https://doi.org/10.1145/1461551.1461591

  86. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: NeurIPS, pp 3104–3112

    Google Scholar 

  87. Ulyanov D, Vedaldi A, Lempitsky V (2018) Deep image prior. In: CVPR, pp 9446–9454

    Google Scholar 

  88. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: NeurIPS

    Google Scholar 

  89. Wang J, Wu C, Xu YQ, Yeung Shum H, Ji L (2002) Learning-based cursive handwriting synthesis. In: Proceedings of the Eighth international workshop on frontiers of handwriting recognition, pp 157–162

    Google Scholar 

  90. Wang J, Wu C, Xu HY, Ying-Qing nd Shum, (2005) Combining shape and physical models for online cursive handwriting synthesis. Int J Doc Anal Recogn (IJDAR) 7(4):219–227. https://doi.org/10.1007/s10032-004-0131-6

  91. Weibel N, Fouse A, Emmenegger C, Friedman W, Hutchins E, Hollan J (2012) Digital pen and paper practices in observational research. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, NY, USA, CHI ’12, pp 1331–1340. https://doi.org/10.1145/2207676.2208590

  92. Williams BH, Toussaint M, Storkey AJ (2007) Modelling motion primitives and their timing in biologically executed movements. In: Proceedings of the 20th international conference on neural information processing systems, Curran associates Inc, USA, NIPS’07, pp 1609–1616. http://dl.acm.org/citation.cfm?id=2981562.2981764

  93. Williams F, Trager M, Panozzo D, Silva C, Zorin D, Bruna J (2019) Gradient dynamics of shallow univariate relu networks. In: NeurIPS, pp 8376–8385

    Google Scholar 

  94. Williams RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Comput

    Google Scholar 

  95. Wu X, Qi Y, Liu J, Yang J (2018) SketchSegNet: a RNN model for labeling sketch strokes. In: MLSP

    Google Scholar 

  96. Xia H, Hinckley K, Pahud M, Tu X, Buxton B (2017) Writlarge: Ink unleashed by unified scope, action, and zoom. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM, New York, NY, USA, CHI ’17, pp 3227–3240. https://doi.org/10.1145/3025453.3025664

  97. Xu P, Hospedales TM, Yin Q, Song YZ, Xiang T, Wang L (2020) Deep learning for free-hand sketch: a survey.

    Google Scholar 

  98. Yang L, Zhuang J, Fu H, Zhou K, Zheng Y (2020) SketchGCN: semantic sketch segmentation with graph convolutional networks

    Google Scholar 

  99. Yoon D, Chen N, Guimbretière F (2013) Texttearing: opening white space for digital ink annotation. In: Proceedings of the 26th annual ACM symposium on user interface software and technology. ACM, New York, NY, USA, UIST ’13, pp 107–112. https://doi.org/10.1145/2501988.2502036

  100. Yoon D, Chen N, Guimbretière F, Sellen A (2014) Richreview: Blending ink, speech, and gesture to support collaborative document review. In: Proceedings of the 27th annual ACM symposium on user interface software and technology. ACM, New York, NY, USA, UIST ’14, pp 481–490. https://doi.org/10.1145/2642918.2647390

  101. Yun XL, Zhang YM, Ye JY, Liu CL (2019) Online handwritten diagram recognition with graph attention networks. In: ICIG

    Google Scholar 

  102. Zanibbi R, Novins K, Arvo J, Zanibbi K (2001) Aiding manipulation of handwritten mathematical expressions through style-preserving morphs. Graph Interf 2001:127–134

    Google Scholar 

  103. Zhang B, Srihari SN, Lee S (2003) Individuality of handwritten characters. In: Proceedings of the 7th international conference on document analysis and recognition, pp 1086–1090

    Google Scholar 

  104. Zitnick CL (2013) Handwriting beautification using token means. ACM Trans Graph 32(4):53:1–53:8. https://doi.org/10.1145/2461912.2461985

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  1. Department of Computer Science, ETH Zürich, Stampfenbachstrasse 48, 8092, Zürich, Switzerland

    Emre Aksan & Otmar Hilliges

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    Yang Li

  2. Advanced Interactive Technologies Lab, ETH Zurich, Zurich, Switzerland

    Otmar Hilliges

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Aksan, E., Hilliges, O. (2021). Generative Ink: Data-Driven Computational Models for Digital Ink. In: Li, Y., Hilliges, O. (eds) Artificial Intelligence for Human Computer Interaction: A Modern Approach. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-030-82681-9_13

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