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Performance analysis of Otsu thresholding for sign language segmentation

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Abstract

Sign language recognition system generally consists of three main processes, which are segmentation, modelling, and classification. Image segmentation plays a crucial role as the initial step in sign language recognition. Despite the many sign language recognition system algorithms proposed in the literature and their well-understood usage, their performance analyses are relatively limited. As such, the main motivation of this paper is to critically analyse the feasibility of successful sign language segmentation under variation of dynamic scene parameters such as noise, hand size, and intensity difference between hand and background. The focus is on image thresholding using Otsu technique, since it is the most commonly used in initial process of sign language segmentation. The analysis of this work was developed based on Monte Carlo statistical method, which showed that the success of sign language segmentation depends on hand size, hand background intensity difference, and noise measurement. The result showed that the sign alphabets with handheld shape like A, E, I, M, N, S, and T is easier to segment, while sign alphabets with finger-extend shape like C, D, F, G, H, K, L, P, R, U, V, W, and Y is harder to segment. Experiment using real images demonstrate the capability of the conditions to correctly predict the outcome of sign language segmentation using Otsu technique. In conclusion, the success of sign language segmentation could be predicted beforehand with obtainable scene parameters.

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Authors and Affiliations

  1. Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia

    Zheng Yu Tan, Shafriza Nisha Basah & Muhammad Juhairi Aziz Safar

  2. Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia

    Haniza Yazid

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  1. Zheng Yu Tan
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Tan, Z.Y., Basah, S.N., Yazid, H. et al. Performance analysis of Otsu thresholding for sign language segmentation. Multimed Tools Appl 80, 21499–21520 (2021). https://doi.org/10.1007/s11042-021-10688-4

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