Fine-tuning a pre-trained Convolutional Neural Network Model to translate American Sign Language in Real-time

Morocho-Cayamcela, Manuel Eugenio; Lim, Wansu

doi:10.1109/iccnc.2019.8685536

Cited by 21 publications

(22 citation statements)

References 9 publications

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“…In transfer learning, we first train a base model on a primary dataset/problem, and then we reuse the deep features, or transfer them, to a second target model which will be trained on a target dataset/problem as in [41]. Fine-tuning is the most common approach to transfer learning and improves the generalization ability of the model used [5]. To this end, the weights of the pre-trained CNN models are fine-tuned by continuing the backpropagation operation.…”

Section: Fine-tuningmentioning

confidence: 99%

A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection

Taşçı

Uluturk

Uğur

2021

Neural Comput & Applic

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Tuberculosis (TB) is known as a potentially dangerous and infectious disease that affects mostly lungs worldwide. The detection and treatment of TB at an early stage are critical for preventing the disease and decreasing the risk of mortality and transmission of it to others. Nowadays, as the most common medical imaging technique, chest radiography (CXR) is useful for determining thoracic diseases. Computer-aided detection (CADe) systems are also crucial mechanisms to provide more reliable, efficient, and systematic approaches with accelerating the decision-making process of clinicians. In this study, we propose voting and preprocessing variations-based ensemble CNN model for TB detection. We utilize 40 different variations in fine-tuned CNN models based on InceptionV3 and Xception by also using CLAHE (contrast-limited adaptive histogram equalization) preprocessing technique and 10 different image transformations for data augmentation types. After analyzing all these combination schemes, three or five best classifier models are selected as base learners for voting operations. We apply the Bayesian optimization-based weighted voting and the average of probabilities as a combination rule in soft voting methods on two TB CXR image datasets to get better results in various numbers of models. The computational results indicate that the proposed method achieves 97.500% and 97.699% accuracy rates on Montgomery and Shenzhen datasets, respectively. Furthermore, our method outperforms state-of-the-art results for the two TB detection datasets in terms of accuracy rate.

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Section: Fine-tuningmentioning

confidence: 99%

A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection

Taşçı

Uluturk

Uğur

2021

Neural Comput & Applic

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“…Each class encompasses 3000 images, with 26 classes corresponding to the 26 American sign language alphabets, and other classes allocated for space, deletion, and nothing. Dataset images are in RBG format with 200 × 3200 pixels dimensions and different variations [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

“…They achieved an accuracy of 94.30%. In the same context, [ 38 ] used the same dataset of 87,000 images for classification. They used (AlexNet and GooLeNet) models, and their overall training results were 99.39% for AlexNet and 95.52% for GoogLeNet.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Technology to Recognize American Sign Language Alphabet

Alsharif,

Altaher,

Altaher

et al. 2023

Sensors

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Historically, individuals with hearing impairments have faced neglect, lacking the necessary tools to facilitate effective communication. However, advancements in modern technology have paved the way for the development of various tools and software aimed at improving the quality of life for hearing-disabled individuals. This research paper presents a comprehensive study employing five distinct deep learning models to recognize hand gestures for the American Sign Language (ASL) alphabet. The primary objective of this study was to leverage contemporary technology to bridge the communication gap between hearing-impaired individuals and individuals with no hearing impairment. The models utilized in this research include AlexNet, ConvNeXt, EfficientNet, ResNet-50, and VisionTransformer were trained and tested using an extensive dataset comprising over 87,000 images of the ASL alphabet hand gestures. Numerous experiments were conducted, involving modifications to the architectural design parameters of the models to obtain maximum recognition accuracy. The experimental results of our study revealed that ResNet-50 achieved an exceptional accuracy rate of 99.98%, the highest among all models. EfficientNet attained an accuracy rate of 99.95%, ConvNeXt achieved 99.51% accuracy, AlexNet attained 99.50% accuracy, while VisionTransformer yielded the lowest accuracy of 88.59%.

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“…Custom datasets are used in this approach to make it more accurate and robust when compared with the models that trained with the less number of datasets [9]. Fingertip finder algorithm is used for gesture recognition which is a combination of convex hull and K-curvature for the hand gesture recognition [10].…”

Section: Related Workmentioning

confidence: 99%

Hand Gesture recognition using Deep Learning

Sangeetha

Srivathsan

Dinesh

et al. 2023

2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS)

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Hearing impaired individuals use sign languages to communicate with others within the community. Because of the wide spread use of this language, hard-of-hearing individuals can easily understand it but it is not known by a lot of normal people. In this paper a hand gesture recognition system has been developed to overcome this problem, for those who don't recognize sign language to communicate simply with hard-of-hearing individuals. In this paper a computer vision-based system is designed to detect sign Language. Datasets used in this paper are binary images. These images are given to the convolution neural network (CNN). This model extracts the features of the image and classifies the images, and it recognises the gestures. The gestures used in this paper are of American Sign Language. In real time system the images are converted to binary images using Hue, Saturation, and Value (HSV) colour model. In this model 87.5% of data is used for training and 12.5% of data is used for testing and the accuracy obtained with this model is 97%.

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Fine-tuning a pre-trained Convolutional Neural Network Model to translate American Sign Language in Real-time

Cited by 21 publications

References 9 publications

A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection

A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection

Deep Learning Technology to Recognize American Sign Language Alphabet

Hand Gesture recognition using Deep Learning

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