Abstract
Leap motion sensor provides a new way of interaction with computers or mobile devices. With this sensor, users can write in air by moving palm or finger, thus, avoiding traditional pen and paper for writing. The strokes of air-writing or 3D writing is different from conventional way of writing. In 3D writing, the words are connected by continuous lines instead of space between them. Also, the arbitrary size of characters and presence of frequent jitters in strokes make the recognition tasks of such words and sentences difficult. To understand the semantics of a word without recognizing each character of words, the alternative process called “word-spotting” is being used. Word-spotting is often useful than conventional recognition systems to understand complex handwriting. Hence, we propose a novel word spotting methodology for 3D text using Leap motion sensor data. Spotting/detection of a keyword in 3D sentences is carried out using Hidden Markov Model (HMM) framework. From experimental study, an average of 41.7 is recorded in terms of Mean-Average-Precision (MAP). The efficiency of the system is demonstrated by comparing traditional segmentation based system. The improved performance shows that the system could be used in developing novel applications in Human-Computer-Interaction (HCI) domain.
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References
Agarwal C, Dogra DP, Saini R, Roy PP (2015) Segmentation and recognition of text written in 3d using leap motion interface. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), November, pp 539–543
Amma C, Georgi M, Schultz T (2012) Airwriting: Hands-free mobile text input by spotting and continuous recognition of 3d-space handwriting with inertial sensors. In: 16Th international symposium on wearable computers, pp 52–59
Baljekar P, Lehman JF, Singh R (2014) Online word-spotting in continuous speech with recurrent neural networks. In: Spoken language technology workshop, pp 536–541
Bassily D, Georgoulas C, Guettler J, Linner T, Bock T (2014) Intuitive and adaptive robotic arm manipulation using the leap motion controller. In: ISR/Robotik 2014; 41st International Symposium on Robotics; Proceedings of, pp 1–7. VDE
Bharath A, Madhvanath S (2012) Hmm-based lexicon-driven and lexicon-free word recognition for online handwritten indic scripts. IEEE PAMI 34 (4):670–682
Behera S, Roy PP, Dogra DP (2018) Fast recognition and verification of 3D air signatures using convex hulls. Expert Syst Appl 100:106–119
Chen M, AlRegib G, Juang BH (2016) Air-writing recognition part i: Modeling and recognition of characters, words, and connecting motions. IEEE Trans Hum Mach Syst 46(3):403–413
Cho OH, Lee ST (2014) A study about honey bee dance serious game for kids using hand gesture. International Journal of Multimedia and Ubiquitous Engineering 9(6):397–404
Chuan CH, Regina E, Guardino C (2014) American sign language recognition using leap motion sensor. In: Machine learning and applications (ICMLA), 2014 13th international conference on, pp 541–544. IEEE
Das A, Bhunia AK, Roy PP, Pal U (2015) Handwritten word spotting in indic scripts using foreground and background information. In: 3Rd IAPR asian conference on pattern recognition, pp 426–430
Espana-Boquera S, Castro-Bleda MJ, Gorbe-Moya J, Zamora-Martinez F (2011) Improving offline handwritten text recognition with hybrid hmm/ann models. IEEE PAMI 33(4):767–779
Fischer A, Keller A, Frinken V, Bunke H (2010) Hmm-based word spotting in handwritten documents using subword models. In: 20Th international conference on pattern recognition, pp 3416–3419
Fischer A, Keller A, Frinken V, Bunke H (2012) Lexicon-free handwritten word spotting using character hmms. Pattern Recogn Lett 33(7):934–942
Frinken V, Fischer A, Manmatha R, Bunke H (2012) A novel word spotting method based on recurrent neural networks. IEEE PAMI 34(2):211–224
Jaeger S, Manke S, Reichert J, Waibel A (2001) Online handwriting recognition: the npen++ recognizer. Int J Doc Anal Recognit 3(3):169–180
Khademi M, Mousavi Hondori H, McKenzie A, Dodakian L, Lopes CV, Cramer SC (2014) Free-hand interaction with leap motion controller for stroke rehabilitation. In: Proceedings of the extended abstracts of the 32nd annual ACM conference on Human factors in computing systems, pp 1663–1668. ACM
Kovalchuk A, Wolf L, Dershowitz N (2014) A simple and fast word spotting method. In: 14Th international conference on frontiers in handwriting recognition, pp 3–8
Kumar P, Saini R, Behera SK, Dogra DP, Roy PP (2017) Real-time recognition of sign language gestures and air-writing using leap motion. In: 2017 Fifteenth IAPR international conference on machine vision applications (MVA), pp 157–160. IEEE
Kumar P, Saini R, Roy P, Dogra D (2017) Study of text segmentation and recognition using leap motion sensor. IEEE Sensors J 17(5):1293–1301
Kumar P, Saini R, Roy PP (2016) Dogra, D.P.: 3d text segmentation and recognition using leap motion. Multimedia Tools and Applications 76:16491–16510
Lai CS, Shi BE (2001) A one-pass strategy for keyword spotting and verification. In: International conference on acoustics, speech, and signal processing, vol. 1, pp 377–380
Mittal A, Kumar P, Roy PP, Balasubramanian R, Chaudhuri BB (2019) A modified LSTM model for continuous sign language recognition using leap motion., 19(16), 7056–7063. IEEE Sensors J 19(16):7056–7063
Markussen A, Jakobsen MR, Hornbæk K (2014) Vulture: a mid-air word-gesture keyboard. In: 32Nd conference on human factors in computing systems, pp 1073–1082
Motion L (2015) Leap motion controller. https://www.leapmotion.com
Mukherjee S, Ahmed SA, Dogra DP, Kar S, Roy PP (2019) Fingertip detection and tracking for recognition of air-writing in videos. Expert Syst Appl 136:217–229
Nigam I, Vatsa M, Singh R (2014) Leap signature recognition using hoof and hot features. In: Image processing (ICIP), 2014 IEEE international conference on, pp 5012–5016. IEEE
Potter LE, Araullo J, Carter L (2013) The leap motion controller: a view on sign language. In: Proceedings of the 25th Australian computer-human interaction conference: augmentation, application, innovation, collaboration, pp 175–178. ACM
Rodríguez-Serrano JA, Perronnin F (2009) Handwritten word-spotting using hidden markov models and universal vocabularies. Pattern Recogn 42 (9):2106–2116
Roy PP, Bhunia AK, Das A, Dhar P, Pal U (2017) Keyword spotting in doctor’s handwriting on medical prescriptions. Expert Syst Appl 76:113–128
Wang K, Belongie S (2010) Word spotting in the wild. In: European conference on computer vision, pp 591–604
Weichert F, Bachmann D, Rudak B, Fisseler D (2013) Analysis of the accuracy and robustness of the leap motion controller. Sensors 13 (5):6380–6393
Xu N, Wang W, Qu X (2015) On-line sample generation for in-air written chinese character recognition based on leap motion controller. In: Pacific rim conference on multimedia, pp 171–180
Xu N, Wang W, Qu X (2015) Recognition of in-air handwritten chinese character based on leap motion controller. In: International conference on image and graphics, pp 160–168
Zhang Z (2012) Microsoft kinect sensor and its effect. IEEE multimedia 19(2):4–10
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Roy, P.P., Kumar, P., Patidar, S. et al. 3D word spotting using leap motion sensor. Multimed Tools Appl 80, 11671–11689 (2021). https://doi.org/10.1007/s11042-020-10229-5
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DOI: https://doi.org/10.1007/s11042-020-10229-5