Recognition of Uni-Stroke Characters with Hand Movements in 3D Space Using Convolutional Neural Networks

Chang, Won-Du; Matsuoka, Akitaka; Kim, Kyeong-Taek; Shin, Jungpil

doi:10.3390/s22166113

Cited by 2 publications

(2 citation statements)

References 23 publications

(32 reference statements)

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“…A recognition rate of 98.33% is achieved in this approach. Won-Du Chang et al [28] proposed an artificial neural network method to recognize characters written in the air by utilizing uni-stroke-designed characters. The proposed method provided 91.06% accuracy on Arabic numbers and English alphabets drawn by 18 people.…”

Section: Literature Reviewmentioning

confidence: 99%

Highly Accurate Trimesh and PointNet based algorithm for Gesture and Hindi air writing recognition

Sharma

Gupta

Sharma

et al. 2023

Preprint

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Air writing and gesture recognition has been one of the most comprehensive and intensively researched areas due to their wide variety of applications in several domains of Human-Computer Interaction (HCI),such as gaming, medicines, and automobiles. This article presents ahighly accurate point-based algorithm for identifying dynamic air writing and free hand gesture recognition for Devanagari Hindi characters.Usually, the written gestures are limited in nature. In order to create a training data set, we are using Leap Motion Controller (LMC) that generates twenty-four sample points from the gesture drawn. In the pre-processing stage, a trimesh is generated using these sample points; the developed trimesh is then fed as input to PointNet. The algorithm developed measures the similarity of the drawn gesture with the training samples to identify and recognize the gesture drawn using Mahalanobis distance. The validation test indicates that the approach is quite accurate, giving a recognition rate of more than 97%. Comparative studies also show that the methodology performs better when trimesh is used as input for the PointNet and more training samples are used.

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Section: Literature Reviewmentioning

confidence: 99%

Highly Accurate Trimesh and PointNet based algorithm for Gesture and Hindi air writing recognition

Sharma

Gupta

Sharma

et al. 2023

Preprint

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“…Gesture recognition technology can be divided into static gesture recognition technology and dynamic gesture recognition technology according to whether it can recognize gestures based on time series [ 4 ]. At present, there are two main ways to collect gesture data, which are non-contact, based on machine vision sensors, and contact, based on data gloves [ 7 ]. The mainstream data-acquisition gloves are divided into three categories, as shown in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

MEMS Devices-Based Hand Gesture Recognition via Wearable Computing

Miao

Gao

et al. 2023

Micromachines

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Gesture recognition has found widespread applications in various fields, such as virtual reality, medical diagnosis, and robot interaction. The existing mainstream gesture-recognition methods are primarily divided into two categories: inertial-sensor-based and camera-vision-based methods. However, optical detection still has limitations such as reflection and occlusion. In this paper, we investigate static and dynamic gesture-recognition methods based on miniature inertial sensors. Hand-gesture data are obtained through a data glove and preprocessed using Butterworth low-pass filtering and normalization algorithms. Magnetometer correction is performed using ellipsoidal fitting methods. An auxiliary segmentation algorithm is employed to segment the gesture data, and a gesture dataset is constructed. For static gesture recognition, we focus on four machine learning algorithms, namely support vector machine (SVM), backpropagation neural network (BP), decision tree (DT), and random forest (RF). We evaluate the model prediction performance through cross-validation comparison. For dynamic gesture recognition, we investigate the recognition of 10 dynamic gestures using Hidden Markov Models (HMM) and Attention-Biased Mechanisms for Bidirectional Long- and Short-Term Memory Neural Network Models (Attention-BiLSTM). We analyze the differences in accuracy for complex dynamic gesture recognition with different feature datasets and compare them with the prediction results of the traditional long- and short-term memory neural network model (LSTM). Experimental results demonstrate that the random forest algorithm achieves the highest recognition accuracy and shortest recognition time for static gestures. Moreover, the addition of the attention mechanism significantly improves the recognition accuracy of the LSTM model for dynamic gestures, with a prediction accuracy of 98.3%, based on the original six-axis dataset.

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Recognition of Uni-Stroke Characters with Hand Movements in 3D Space Using Convolutional Neural Networks

Cited by 2 publications

References 23 publications

Highly Accurate Trimesh and PointNet based algorithm for Gesture and Hindi air writing recognition

Highly Accurate Trimesh and PointNet based algorithm for Gesture and Hindi air writing recognition

MEMS Devices-Based Hand Gesture Recognition via Wearable Computing

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