Skip to main content
SpringerLink
Log in

Fingertip-writing alphanumeric character recognition based on hidden conditional random field

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

This paper proposes a fingertip handwriting alphanumeric character recognition system based on hidden conditional random field (HCRF) model to provide an alternative for human–computer interaction. Traditional handwriting recognition systems are limited because they require a specific or expensive input device, such as pen, tablet, or touch panel. Recently, cameras have gradually become standard components in many computer-based products. Therefore, a fingertip and camera combination provides a flexible and convenient input device. This proposed system combines fingertip detection, trajectory feature extraction, and character recognition. First, fingertip moving trajectories are tracked and recoded. Then, the stroke features are extracted from the trajectories as features. Finally, the proposed system adopts the HCRF model to recognize handwritten characters. Experimental results show that the proposed novel input system is feasible and effective. This study also presents possible applications for camera input devices.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Elgammal A, Duraiswami R, Harwood D, Davis LS (2002) Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc IEEE 90(7):1151–1163

    Article  Google Scholar 

  • Gunawardana A, Mahajan M, Acero A, Platt JC (2005) Hidden conditional random fields for phone classification. In: Proc. INTERSPEECH

  • Jin Lianwen, Chen Caihong, Zhen Lixin, Huang Jiancheng (2005) Real-time fingertip detection from cluttered background for vision-based HCI. J Commun Comput 2(9):1–8

    Google Scholar 

  • Kumar S, Hebert M (2003) Discriminative random fields: a framework for contextual interaction in classification. In: Proc. of the IEEE International Conference on Computer Vision: 1150–1157

  • Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proc. ICML

  • Lampson BW (2007) Pointing system for pen-based computer. US Patent 7 248 248

    Google Scholar 

  • Lee DS (2005) Effective gaussian mixture learning for video background subtraction. IEEE Trans Pattern Anal Mach Intell 27(5):827–832

    Article  Google Scholar 

  • Liu Y, Liu X, Jia Y (2006) Hand-gesture based text input for wearable computers. Proceedings of the Fourth IEEE International Conference on Computer Vision Systems: 8–13

  • Mutter S, Pfahringer B (2007) A discriminative approach to structured biological data. In: Proc. of the 5th New Zealand Computer Science Research Student Conference

  • Quattoni A, Collins M, Darrell T (2004) Conditional random fields for object recognition. In: Proc. NIPS

  • Richard DGS, Duda O, Hart PE (2001) Pattern Classification. Wiley, New Jersey

  • Sha F, Pereira F (2003) Shallow parsing with conditional random fields. In: Proc. of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language

  • Sobottka K, Pitas I (1998) A novel method for automatic face segmentation, facial feature extraction and tracking. Signal process. Image Commun 12:263–281

    Google Scholar 

  • Sung Yun-Hsuan, Dan Jurafsky (2009) Hidden Conditional Random Fields for Phone Recognition. In: Proc. of IEEE Automatic Speech Recognition and Understanding Workshop

  • Sy BW, Quattoni A, Morency LP, Demirdjian D, Darrell T (2006) Hidden conditional random fields for gesture recognition. In: Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition: 1521–1527

  • Tavakkoli A, Nicolescu M, Bebis G, Nicolescu M (2008) Non-parametric statistical background modeling for efficient foreground region detection. Mach Vis Appl 20(6):395–409

    Article  Google Scholar 

  • Vezhnevets V, Sazonov V, Andreeva A (2003) A survey on pixel- based skin color detection techniques. Proc. GRAPHICON03, Moscow, Russia: 85–92

  • Zhang Yang, Shi Guangshun, Yang Jufeng (2009) HMM-based online recognition of handwritten chemical symbols. In: Proc. of the International Conference on Document Analysis and Recognition, ICDAR: 1255–1259

Download references

Acknowledgments

We would like to thank the National Science Council (Grant number: NSC 99-2628-E-155 -055 -MY2) for supporting this work.

Author information

Authors and Affiliations

  1. Department of Communications Engineering, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, Taoyuan County 320, Taiwan

    Chien-Cheng Lee & Yi-Fang Li

Authors
  1. Chien-Cheng Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi-Fang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chien-Cheng Lee.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, CC., Li, YF. Fingertip-writing alphanumeric character recognition based on hidden conditional random field. J Ambient Intell Human Comput 4, 285–291 (2013). https://doi.org/10.1007/s12652-011-0092-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-011-0092-9

Keywords

Search

Navigation