article · Big Data Mining and Analytics
Hand gesture recognition systems are increasingly important for modern human-computer interfaces and sign language recognition. Conventional systems struggle with large dimensional datasets, high computational time, elevated false positives, and misclassification errors. To address these limitations, a pipeline combines image segmentation, feature selection, and classification techniques. The method begins by segmenting the hand gesture portion of an image using skin colour detection and morphological operations. Next, a Heuristic Manta-ray Foraging Optimisation algorithm selects optimal features by assessing fitness values, effectively reducing data dimensionality. This reduction in feature dimensions aims to lower error rates and improve overall classification accuracy. Finally, an Adaptive Extreme Learning Machine predicts the hand gesture recognition output. Performance validation using multiple evaluation measures demonstrates comparative outcomes against existing classification models.
Hand gesture recognition is vital for intuitive human-computer interaction and for facilitating communication between speech- and hearing-impaired communities through sign language interpretation. Improving the accuracy and processing speed of these systems helps eliminate errors, bringing automated recognition systems closer to reliable real-time use in everyday assistive and computing technologies.
The method applies directly to assistive communication tools, such as automated sign language translators, and interactive human-computer interface systems. Potential users include software developers of accessibility technologies and smart device interfaces. Based on the abstract, the research is an early-stage algorithmic framework evaluated through computational performance metrics, requiring further system integration and testing before reaching practical, real-world deployment.
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The development of hand gesture recognition systems has gained more attention in recent days, due to its support of modern human-computer interfaces. Moreover, sign language recognition is mainly developed for enabling communication between deaf and dumb people. In conventional works, various image processing techniques like segmentation, optimization, and classification are deployed for hand gesture recognition. Still, it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption, increased false positives, error rate, and misclassification outputs. Hence, this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques. During image segmentation, skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion. Then, the Heuristic Manta-ray Foraging Optimization (HMFO) technique is employed for optimally selecting the features by computing the best fitness value. Moreover, the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate. Finally, an Adaptive Extreme Learning Machine (AELM) based classification technique is employed for predicting the recognition output. During results validation, various evaluation measures have been used to compare the proposed model's performance with other classification approaches.
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DOI: 10.26599/bdma.2022.9020036
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