Abstract—Human Computer Interaction (HCI) focuses on
the interaction between humans and machines. An extensive list
of applications exists for hand gesture recognition techniques,
major candidates for HCI. The list covers various fields, one of
which is sign language recognition. In this field, however, high
accuracy and robustness are both needed; both present a major
challenge. In addition, feature extraction from hand gesture
images is a tough task because of the many parameters
associated with them. This paper proposes an approach based
on a bag-of-words (BoW) model for automatic recognition of
American Sign Language (ASL) numbers. In this method, the
first step is to obtain the set of representative vocabularies by
applying a K-means clustering algorithm to a few randomly
chosen images. Next, the vocabularies are used as bin centers
for BoW histogram construction. The proposed histograms are
shown to provide distinguishable features for classification of
ASL numbers. For the purpose of classification, the K-nearest
neighbors (kNN) classifier is employed utilizing the BoW
histogram bin frequencies as features. For validation, very large
experiments are done on two large ASL number-recognition
datasets; the proposed method shows superior performance in
classifying the numbers, achieving an F1 score of 99.92% in the
Kaggle ASL numbers dataset.
Index Terms—Human computer interaction (HCI), hand gesture recognition (HGR), American sign language (ASL), bag-of-words (BoW), kNN classifier.
Rasel Ahmed Bhuiyan and Abdul Matin are with the Department of Computer Science and Engineering, Uttara University, Dhaka, Bangladesh (e-mail: email@example.com, firstname.lastname@example.org).
Md. Shafiur Raihan Shafi is with the Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh (e-mail: email@example.com).
Amit Kumar Kundu was with the Department of Electrical and Electronics Engineering, Uttara University, Dhaka, Bangladesh (e-mail: firstname.lastname@example.org).
Cite: Rasel Ahmed Bhuiyan, Abdul Matin, Md. Shafiur Raihan Shafi, and Amit Kumar Kundu, "A Bag-of-Words Based Feature Extraction Scheme for American Sign Language Number Recognition from Hand Gesture Images," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 85-91, 2021.Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).