From Parallel Sequence Representations to Calligraphic Control: A Conspiracy of Neural Circuits

Calligraphic writing presents a rich set of challenges to the human movement control system. These challenges include: initial learning, and recall from memory, of prescribed stroke sequences; critical timing of stroke onsets and durations; fine control of grip and contact forces; and letter-form invariance under voluntary size scaling, which entails fine control of stroke direction and amplitude during recruitment and derecruitment of musculoskeletal degrees of freedom. Experimental and computational studies in behavioral neuroscience have made rapid progress toward explaining the learning, planning and contTOl exercised in tasks that share features with calligraphic writing and drawing. This article summarizes computational neuroscience models and related neurobiological data that reveal critical operations spanning from parallel sequence representations to fine force control. Part one addresses stroke sequencing. It treats competitive queuing (CQ) models of sequence representation, performance, learning, and recall. Part two addresses letter size scaling and motor equivalence. It treats cursive handwriting models together with models in which sensory-motor tmnsformations are performed by circuits that learn inverse differential kinematic mappings. Part three addresses fine-grained control of timing and transient forces, by treating circuit models that learn to solve inverse dynamics problems.National Institutes of Health (R01 DC02852

Using the MGGI Methodology for Category-based Language Modeling in Handwritten Marriage Licenses Books

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Handwritten marriage licenses books have been used for centuries by ecclesiastical and secular institutions to register marriages. The information contained in these historical documents is useful for demography studies and genealogical research, among others. Despite the generally simple structure of the text in these documents, automatic transcription and semantic information extraction is difficult due to the distinct and evolutionary vocabulary, which is composed mainly of proper names that change along the time. In previous works we studied the use of category-based language models to both improve the automatic transcription accuracy and make easier the extraction of semantic information. Here we analyze the main causes of the semantic errors observed in previous results and apply a Grammatical Inference technique known as MGGI to improve the semantic accuracy of the language model obtained. Using this language model, full handwritten text recognition experiments have been carried out, with results supporting the interest of the proposed approach.This work has been partially supported through the European Union’s H2020 grant READ (Ref: 674943), the European project ERC-2010-AdG-20100407-269796, the MINECO/FEDER, UE projects TIN2015-70924-C2-1-R and TIN2015-70924-C2-2-R, and the Ramon y Cajal Fellowship RYC-2014-16831.Romero Gómez, V.; Fornes, A.; Vidal Ruiz, E.; Sánchez Peiró, JA. (2016). Using the MGGI Methodology for Category-based Language Modeling in Handwritten Marriage Licenses Books. IEEE. https://doi.org/10.1109/ICFHR.2016.0069

Multiple Input Modes Without Specific Mode Selection

A system and method are disclosed that enables multiple text input modes for a device without explicitly specifying the desired mode. A machine learning (ML) model is used to handle and interpret the inputs (text, voice, handwritten, etc.). The ML model analyzes sequences of data, and trains itself so that the correct final output sequence is given to the application requiring the text input. A decoder is used to combine the output of the sequence interpretation model with other knowledge sources such as character or word recognition models. Then, a language-model is used in a decoder to obtain the most likely sequence of words or characters given all user inputs. The recognition of the most likely inputs improves and enables automatic mode selection, eliminating explicit segmentation between the modalities

An integrated grammar-based approach for mathematical expression recognition

This is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition 51 (2016) 135–147. DOI 10.1016/j.patcog.2015.09.013.Automatic recognition of mathematical expressions is a challenging pattern recognition problem since there are many ambiguities at different levels. On the one hand, the recognition of the symbols of the mathematical expression. On the other hand, the detection of the two-dimensional structure that relates the symbols and represents the math expression. These problems are closely related since symbol recognition is influenced by the structure of the expression, while the structure strongly depends on the symbols that are recognized. For these reasons, we present an integrated approach that combines several stochastic sources of information and is able to globally determine the most likely expression. This way, symbol segmentation, symbol recognition and structural analysis are simultaneously optimized. In this paper we define the statistical framework of a model based on two-dimensional grammars and its associated parsing algorithm. Since the search space is too large, restrictions are introduced for making the search feasible. We have developed a system that implements this approach and we report results on the large public dataset of the CROHME international competition. This approach significantly outperforms other proposals and was awarded best system using only the training dataset of the competition. (C) 2015 Elsevier Ltd. All rights reserved.This work was partially supported by the Spanish MINECO under the STraDA research project (TIN2012-37475-C02-01) and the FPU Grant (AP2009-4363).Álvaro Muñoz, F.; Sánchez Peiró, JA.; Benedí Ruiz, JM. (2016). An integrated grammar-based approach for mathematical expression recognition. Pattern Recognition. 51:135-147. https://doi.org/10.1016/j.patcog.2015.09.013S1351475

Handwriting recognition by using deep learning to extract meaningful features

[EN] Recent improvements in deep learning techniques show that deep models can extract more meaningful data directly from raw signals than conventional parametrization techniques, making it possible to avoid specific feature extraction in the area of pattern recognition, especially for Computer Vision or Speech tasks. In this work, we directly use raw text line images by feeding them to Convolutional Neural Networks and deep Multilayer Perceptrons for feature extraction in a Handwriting Recognition system. The proposed recognition system, based on Hidden Markov Models that are hybridized with Neural Networks, has been tested with the IAM Database, achieving a considerable improvement.Work partially supported by the Spanish MINECO and FEDER founds under project TIN2017-85854-C4-2-R.Pastor Pellicer, J.; Castro-Bleda, MJ.; España Boquera, S.; Zamora-Martinez, FJ. (2019). Handwriting recognition by using deep learning to extract meaningful features. AI Communications. 32(2):101-112. https://doi.org/10.3233/AIC-170562S101112322Baldi, P., Brunak, S., Frasconi, P., Soda, G., & Pollastri, G. (1999). Exploiting the past and the future in protein secondary structure prediction. Bioinformatics, 15(11), 937-946. doi:10.1093/bioinformatics/15.11.937LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539Bertolami, R., & Bunke, H. (2008). Hidden Markov model-based ensemble methods for offline handwritten text line recognition. Pattern Recognition, 41(11), 3452-3460. doi:10.1016/j.patcog.2008.04.003Bianne-Bernard, A.-L., Menasri, F., Mohamad, R. A.-H., Mokbel, C., Kermorvant, C., & Likforman-Sulem, L. (2011). Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10), 2066-2080. doi:10.1109/tpami.2011.22C.M. Bishop, Neural networks for pattern recognition, Oxford University Press, 1995.T. Bluche, H. Ney and C. Kermorvant, Feature extraction with convolutional neural networks for handwritten word recognition, in: 12th International Conference on Document Analysis and Recognition (ICDAR), 2013, pp. 285–289.T. Bluche, H. Ney and C. Kermorvant, Tandem HMM with convolutional neural network for handwritten word recognition, in: 38th International Conference on Acoustics Speech and Signal Processing (ICASSP), 2013, pp. 2390–2394.T. Bluche, H. Ney and C. Kermorvant, A comparison of sequence-trained deep neural networks and recurrent neural networks optical modeling for handwriting recognition, in: Slsp-2014, 2014, pp. 1–12.H. Bourlard and N. Morgan, Connectionist Speech Recognition – A Hybrid Approach, Series in Engineering and Computer Science, Vol. 247, Kluwer Academic, 1994.Bozinovic, R. M., & Srihari, S. N. (1989). Off-line cursive script word recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(1), 68-83. doi:10.1109/34.23114H. Bunke, Recognition of cursive roman handwriting – past, present and future, in: International Conference on Document Analysis and Recognition, Vol. 1, 2003, pp. 448–459.E. Caillault, C. Viard-Gaudin and A. Rahim Ahmad, MS-TDNN with global discriminant trainings, in: International Conference on Document Analysis and Recognition (ICDAR), 2005, pp. 856–860.P. Doetsch, M. Kozielski and H. Ney, Fast and robust training of recurrent neural networks for offline handwriting recognition, in: 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2014, pp. 279–284.P. Dreuw, P. Doetsch, C. Plahl and H. Ney, Hierarchical hybrid MLP/HMM or rather MLP features for a discriminatively trained Gaussian HMM: A comparison for offline handwriting recognition, in: International Conference on Image Processing (ICIP), 2011, pp. 3541–3544.Dreuw, P., Heigold, G., & Ney, H. (2011). Confidence- and margin-based MMI/MPE discriminative training for off-line handwriting recognition. International Journal on Document Analysis and Recognition (IJDAR), 14(3), 273-288. doi:10.1007/s10032-011-0160-xEspaña-Boquera, S., Castro-Bleda, M. J., Gorbe-Moya, J., & Zamora-Martinez, F. (2011). Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(4), 767-779. doi:10.1109/tpami.2010.141A. Graves, S. Fernández, F. Gomez and J. Schmidhuber, Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks, in: 23rd International Conference on Machine Learning (ICML), ACM, 2006, pp. 369–376.A. Graves and N. Jaitly, Towards end-to-end speech recognition with recurrent neural networks, in: 31st International Conference on Machine Learning (ICML), 2014, pp. 1764–1772.Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., & Schmidhuber, J. (2009). A Novel Connectionist System for Unconstrained Handwriting Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 855-868. doi:10.1109/tpami.2008.137A. Graves and J. Schmidhuber, Framewise phoneme classification with bidirectional LSTM networks, in: International Joint Conference on Neural Networks (IJCNN), Vol. 4, 2005, pp. 2047–2052.A. Graves and J. Schmidhuber, Offline handwriting recognition with multidimensional recurrent neural networks, in: Advances in Neural Information Processing Systems (NIPS), 2009, pp. 545–552.F. Grézl, M. Karafiát, S. Kontár and J. Černocký, Probabilistic and bottle-neck features for LVCSR of meetings, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol. 4, 2007.Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735Impedovo, S. (2014). More than twenty years of advancements on Frontiers in handwriting recognition. Pattern Recognition, 47(3), 916-928. doi:10.1016/j.patcog.2013.05.027Jaeger, S., Manke, S., Reichert, J., & Waibel, A. (2001). Online handwriting recognition: the NPen++ recognizer. International Journal on Document Analysis and Recognition, 3(3), 169-180. doi:10.1007/pl00013559M. Kozielski, P. Doetsch and H. Ney, Improvements in RWTH’s system for off-line handwriting recognition, in: 12th International Conference on Document Analysis and Recognition (ICDAR), IEEE, 2013, pp. 935–939.A. Krizhevsky, I. Sutskever and G.E. Hinton, ImageNet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems (NIPS), F. Pereira, C.J.C. Burges, L. Bottou and K.Q. Weinberger, eds, Vol. 25, Curran Associates, Inc., 2012, pp. 1097–1105.Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. doi:10.1109/5.726791M. Liwicki, A. Graves, H. Bunke and J. Schmidhuber, A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks, in: 9th International Conference on Document Analysis and Recognition (ICDAR), 2007, pp. 367–371.Marti, U.-V., & Bunke, H. (2002). The IAM-database: an English sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition, 5(1), 39-46. doi:10.1007/s100320200071S. Marukatat, T. Artieres, R. Gallinari and B. Dorizzi, Sentence recognition through hybrid neuro-Markovian modeling, in: 6th International Conference on Document Analysis and Recognition (ICDAR), 2001, pp. 731–735.F.J. Och, Minimum error rate training in statistical machine translation, in: 41st Annual Meeting on Association for Computational Linguistics, ACL’03, Vol. 1, 2003, pp. 160–167.J. Pastor-Pellicer, S. España-Boquera, M.J. Castro-Bleda and F. Zamora-Martínez, A combined convolutional neural network and dynamic programming approach for text line normalization, in: 13th International Conference on Document Analysis and Recognition (ICDAR), 2015.J. Pastor-Pellicer, S. España-Boquera, F. Zamora-Martínez, M. Zeshan Afzal and M.J. Castro-Bleda, Insights on the use of convolutional neural networks for document image binarization, in: The International Work-Conference on Artificial Neural Networks, Vol. 9095, 2015, pp. 115–126.V. Pham, T. Bluche, C. Kermorvant and J. Louradour, Dropout improves recurrent neural networks for handwriting recognition, in: International Conference on Frontiers in Handwriting Recognition (ICFHR), 2014, pp. 285–290.Plamondon, R., & Srihari, S. N. (2000). Online and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 63-84. doi:10.1109/34.824821Plötz, T., & Fink, G. A. (2009). Markov models for offline handwriting recognition: a survey. International Journal on Document Analysis and Recognition (IJDAR), 12(4), 269-298. doi:10.1007/s10032-009-0098-4A. Poznanski and L. Wolf, CNN-N-gram for HandwritingWord recognition, in: Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2305–2314.Puigcerver, J. (2017). Are Multidimensional Recurrent Layers Really Necessary for Handwritten Text Recognition? 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). doi:10.1109/icdar.2017.20L.R. Rabiner, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, 1989.Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., … Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211-252. doi:10.1007/s11263-015-0816-yT.N. Sainath, B. Kingsbury and B. Ramabhadran, Auto-encoder bottleneck features using deep belief networks, in: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2012, pp. 4153–4156.Sayre, K. M. (1973). Machine recognition of handwritten words: A project report. Pattern Recognition, 5(3), 213-228. doi:10.1016/0031-3203(73)90044-7Schenkel, M., Guyon, I., & Henderson, D. (1995). On-line cursive script recognition using time-delay neural networks and hidden Markov models. Machine Vision and Applications, 8(4), 215-223. doi:10.1007/bf01219589Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673-2681. doi:10.1109/78.650093A.W. Senior and A.J. Robinson, An off-line cursive handwriting recognition system, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, 1998, pp. 309–321.E. Singer and R.P. Lippman, A speech recognizer using radial basis function neural networks in an HMM framework, in: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 1, IEEE, 1992, pp. 629–632.J. Stadermann, A hybrid SVM/HMM acoustic modeling approach to automatic speech recognition, in: International Conference on Spoken Language Processing (ICSLP), 2004.A. Stolcke, SRILM: An extensible language modeling toolkit, in: International Conference on Spoken Language Processing (ICSLP), 2002, pp. 901–904.C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, Going deeper with convolutions, in: Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1–12.TOSELLI, A. H., JUAN, A., GONZÁLEZ, J., SALVADOR, I., VIDAL, E., CASACUBERTA, F., … NEY, H. (2004). INTEGRATED HANDWRITING RECOGNITION AND INTERPRETATION USING FINITE-STATE MODELS. International Journal of Pattern Recognition and Artificial Intelligence, 18(04), 519-539. doi:10.1142/s0218001404003344Toselli, A. H., Romero, V., Pastor, M., & Vidal, E. (2010). Multimodal interactive transcription of text images. Pattern Recognition, 43(5), 1814-1825. doi:10.1016/j.patcog.2009.11.019J.M. Vilar, Efficient computation of confidence intervals for word error rates, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2008, pp. 5101–5104.Vinciarelli, A. (2002). A survey on off-line Cursive Word Recognition. Pattern Recognition, 35(7), 1433-1446. doi:10.1016/s0031-3203(01)00129-7Voigtlaender, P., Doetsch, P., & Ney, H. (2016). Handwriting Recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks. 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR). doi:10.1109/icfhr.2016.0052E. Wang, Q. Zhang, B. Shen, G. Zhang, X. Lu, Q. Wu and Y. Wang, Intel math kernel library, in: High-Performance Computing on the Intel® Xeon Phi™, Springer, 2014, pp. 167–188.F. Zamora-Martínez et al., April-ANN Toolkit, a Pattern Recognizer in Lua, Artificial Neural Networks Module, 2013, https://github.com/pakozm/ [github.com]april-ann.Zamora-Martínez, F., Frinken, V., España-Boquera, S., Castro-Bleda, M. J., Fischer, A., & Bunke, H. (2014). Neural network language models for off-line handwriting recognition. Pattern Recognition, 47(4), 1642-1652. doi:10.1016/j.patcog.2013.10.020Zeyer, A., Beck, E., Schlüter, R., & Ney, H. (2017). CTC in the Context of Generalized Full-Sum HMM Training. Interspeech 2017. doi:10.21437/interspeech.2017-107

2D Grammar Extension of the CMP Mathematical Formulae On-line Recognition System

Projecte realitzat en col.laboració amb Czech Technical University in PragueIn the last years, the recognition of handwritten mathematical formulae has recieved an increasing amount of attention in pattern recognition research. However, the diversity of approaches to the problem and the lack of a commercially viable system indicate that there is still much research to be done in this area. In this thesis, I will describe the previous work on a system for on-line handwritten mathematical formulae recognition based on the structural construction paradigm and two-dimensional grammars. In general, this approach can be successfully used in the anaylysis of inputs composed of objects that exhibit rich structural relations. An important benefit of the structural construction is in not treating symbols segmentation and structural anaylsis as two separate processes which allows the system to perform segmentation in the context of the whole formula structure, helping to solve arising ambiguities more reliably. We explore the opening provided by the polynomial complexity parsing algorithm and extend the grammar by many new grammar production rules which made the system useful for formulae met in the real world. We propose several grammar extensions to support a wide range of real mathematical formulae, as well as new features implemented in the application. Our current approach can recognize functions, limits, derivatives, binomial coefficients, complex numbers and more

Escritoire: A Multi-touch Desk with e-Pen Input for Capture, Management and Multimodal Interactive Transcription of Handwritten Documents

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8_53A large quantity of documents used every day are still handwritten. However, it is interesting to transform each of these documents into its digital version for managing, archiving and sharing. Here we present Escritoire, a multi-touch desk that allows the user to capture, transcribe and work with handwritten documents. The desktop is continuously monitored using two cameras. Whenever the user makes a specific hand gesture over a paper, Escritoire proceeds to take an image. Then, the capture is automatically preprocesses, obtaining as a result an improved representation. Finally, the text image is transcribed using automatic techniques and finally the transcription is displayed on Escritoire.This work was partially supported by the Spanish MEC under FPU scholarship (AP2010-0575), STraDA research project (TIN2012-37475-C02-01) and MITTRAL research project (TIN2009-14633-C03-01); the EU’s 7th Framework Programme under tranScriptorium grant agreement (FP7/2007-2013/600707).Martín-Albo Simón, D.; Romero Gómez, V.; Vidal Ruiz, E. (2015). Escritoire: A Multi-touch Desk with e-Pen Input for Capture, Management and Multimodal Interactive Transcription of Handwritten Documents. En Pattern Recognition and Image Analysis. Springer. 471-478. https://doi.org/10.1007/978-3-319-19390-8_53S471478Andrew, A.: Another efficient algorithm for convex hulls in two dimensions. Inf. Process. Lett. 9(5), 216–219 (1979)Bosch, V., Toselli, A.H., Vidal, E.: Statistical text line analysis in handwritten documents. In: Proceedings of ICFHR (2012)Eisenstein, J., Puerta, A.: Adaptation in automated user-interface design. In: Proceedings of International Conference on Intelligent User Interfaces (2000)Jelinek, F.: Statistical Methods for Speech Recognition. MIT Press, Cambridge (1998)Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. 82(Series D), 35–45 (1960)Keysers, D., Shafait, F., Breuel, T.M.: Document image zone classification - a simple high-performance approach. In: Proceedings of International Conference on Computer Vision Theory (2007)Kozielski, M., Forster, J., Ney, H.: Moment-based image normalization for handwritten text recognition. In: Proceedings of ICFHR (2012)Lampert, C.H., Braun, T., Ulges, A., Keysers, D., Breuel, T.M.: Oblivious document capture and real-time retrieval. In: International Workshop on Camera Based Document Analysis and Recognition (2005)Liang, J., Doermann, D., Li, H.: Camera based analysis of text and documents a survey. Int. J. Doc. Anal. Recogn. 7(2–3), 84–104 (2005)Liwicki, M., Rostanin, O., El-Neklawy, S.M., Dengel, A.: Touch & write: a multi-touch table with pen-input. In: Proceedings of International Workshop on Document Analysis Systems (2010)Marti, U.V., Bunke, H.: Text line segmentation and word recognition in a system for general writer independent handwriting recognition. In: Proceedings of ICDAR (2001)Martín-Albo, D., Romero, V., Toselli, A.H., Vidal, E.: Multimodal computer-assisted transcription of text images at character-level interaction. Int. J. Pattern Recogn. Artif. Intell. 26(5), 19 (2012)Martín-Albo, D., Romero, V., Vidal, E.: Interactive off-line handwritten text transcription using on-line handwritten text as feedback. In: Proceedings of ICDAR (2013)Mitra, S., Acharya, T.: Gesture recognition: a survey. IEEE Trans. Syst. Man Cybern. B Cybern. 37(3), 311–324 (2007)Terry, M., Mynatt, E.D.: Recognizing creative needs in user interface design. In: Proceedings of C&C (2002)Toselli, A.H., Juan, A., Keysers, D., González, J., Salvador, I., Ney, H., Vidal, E., Casacuberta, F.: Integrated handwriting recognition and interpretation using finite-state models. Int. J. Pattern Recognit. Artif. Intell. 18(4), 519–539 (2004)Toselli, A.H., Romero, V., Pastor, M., Vidal, E.: Multimodal interactive transcription of text images. Pattern Recognit. 43(5), 1814–1825 (2010)Toselli, A.H., Romero, V., Vidal, E.: Computer assisted transcription of text images and multimodal interaction. In: Popescu-Belis, A., Stiefelhagen, R. (eds.) MLMI 2008. LNCS, vol. 5237, pp. 296–308. Springer, Heidelberg (2008)Wachs, J.P., Kolsch, M., Stern, H., Edan, Y.: Vision-based hand-gesture applications. Commun. ACM. 54(2), 60–71 (2011)Wobbrock, J.O., Morris, M.R., Wilson, A.D.: User-defined gestures for surface computing. In: Proceedings of CHI (2009