Abstract
This paper presents a Histograms of Orientation Gradient (HOG) based feature vector for design of Sign Language Recognition System (SLRS). HOG is known to be independent of segmentation task. In this work an attempt has been made to explore the need and motivation to recognize ISL, which can provide opportunities for hearing impaired person in working environment to become more self reliant. For experimentation, Indian Sign Language (ISL) uniform background alphabet dataset and the Triesch’s database has been taken. Triesch’s database comprises of hand gesture recognition images in uniform as well as complex background condition. The optimal feature vector is computed by identifying the HOG parameters which vary the feature vector size. By repeated experimentation, the feature vector size has been reduced by increasing the number of pixels per cell without compromising accuracy. Performance evaluation has been done on the basis of accuracy for different classifiers such as Support Vector Machine (SVM), Naïve Bayes (NB) and Simple Logistic. For ISL dataset, SVM exhibits the best performance with highest accuracy of 94.5%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mitra, S., Acharya, T.: Gesture recognition: a review. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 37, 311–324 (2007)
Melnyk, M., Shadrova, V., Karwatsky, B.: Towards computer assisted international sign language recognition system: a systematic survey. Int. J. Comput. Appl. 89(17), 44–51 (2014)
Ong, S.C.W., Ranganath, S.: Automatic sign language analysis: a survey and the further beyond lexical meaning. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 837–891 (2007)
Badhe, P., Kulkarni, V.: Indian sign language translator using gesture recognition algorithm. In: IEEE International Conference on Computer Graphics, Vision and Information Security, pp. 195–200 (2015)
Mullur, K.M.: Indian sign language recognition system. Int. J. Eng. Trends Technol. 21(9), 450–454 (2015)
Joshi, G., Vig, R., Singh, S.: CFS-Infogain based combined shape based feature vector for signer independent ISL database. In: Proceedings of 6th International Conference on Pattern Recognition Applications and Methods, pp. 541–548 (2017)
Viswanathan, D.M., Idicula, S.M.: Recent developments in Indian sign language recognition: analysis. IEEE Int. J. Comput. Sci. Inf. Technol. 6, 289–293 (2015)
Rekha, J., Bhattacharya, J., Majumder, S.: Shape, texture and local movement hand gesture features for indian sign language. In: 3rd International Conference on Trendz in Information Sciences and Computing (TISC), pp. 30–35 (2011)
Adithya, V., Vinod, P.R., Gopalakrishnan, U.: Artificial neural network based method for indian sign language recognition. In: IEEE Conference on Information and Communication Technologies, pp. 1080–1086 (2013)
Collumeau, J.F., Leconge, R., Emile, B., Laurent, H.: Hand-gesture recognition: comparative study of global, semi-local and local approaches. In: 7th International Symposium on Image and Signal Processing and Analysis, pp. 247–253 (2011)
Gupta, S., Shukla, P., Mittal, A.: K-nearest correlated neighbor classification for Indian sign language gesture recognition using feature fusion. In: International Conference on Computer Communication and Informatics, pp. 1–5 (2016)
Kaur, B., Joshi, G.: Lower order Krawtchouk moment-based feature-set for hand gesture recognition. Adv. Hum. Comput. Interact. 2016, 1–10 (2016)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2005, San Diego, USA, pp. 886–893 (2005)
Chen, J., et al.: WLD: a robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1705–1720 (2010)
Déniz, O., Bueno, G., De la Torre, F.: Face recognition using histograms of oriented gradients. Pattern Recogn. Lett. 32(12), 1598–1603 (2011)
Albiol, A., Monzo, D., Martin, A., Sastre, J., Albiol, A.: Face recognition using HOG–EBGM. Pattern Recogn. Lett. 29(10), 1537–1543 (2008)
Škrabánek, P., Dolezel, P.: Robust grape detector based on SVMs and HOG features. Comput. Intell. Neurosci. 2017, 1–18 (2017)
Feng, K., Yuan, F.: Static hand gesture recognition based on HOG characters and support VectorMachines. In: 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation, pp. 936–938 (2013)
Lin, J., Ding, Y.: A temporal hand gesture recognition system based on HOG and motion trajectory. Optik 124, 6795–6798 (2010)
Pang, Y., Yuan, Y., Li, X., Pan, J.: Efficient HOG human detection. Sig. Process. 91, 773–781 (2011)
Kaur, B., Joshi, G., Vig, R.: Indian sign language recognition using Krawtchouk moment-based local features. Imaging Sci. J. 65(3), 171–179 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Joshi, G., Gaur, A., Sheenu (2018). Interpretation of Indian Sign Language Using Optimal HOG Feature Vector. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 905. Springer, Singapore. https://doi.org/10.1007/978-981-13-1810-8_7
Download citation
DOI: https://doi.org/10.1007/978-981-13-1810-8_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1809-2
Online ISBN: 978-981-13-1810-8
eBook Packages: Computer ScienceComputer Science (R0)