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Tag: Convolution
Optimized Convolution Neural Network (OCNN) for VBSLR: Optimized Convolution Neural Network (OCNN) for Voice-Based Sign Language Recognition: Optimization & Regularization
Price: $65.00
(as of Dec 27,2024 01:02:49 UTC – Details)
Publisher : LAP LAMBERT Academic Publishing (June 29, 2021)
Language : English
Paperback : 120 pages
ISBN-10 : 6203927937
ISBN-13 : 978-6203927931
Item Weight : 6.9 ounces
Dimensions : 5.91 x 0.28 x 8.66 inches
Voice-based sign language recognition (VBSLR) is a cutting-edge technology that aims to bridge the communication gap between individuals who are deaf or hard of hearing and those who are not proficient in sign language. One of the key components of VBSLR is the Convolution Neural Network (CNN), a deep learning algorithm that has shown promising results in image and speech recognition tasks.In order to enhance the performance and efficiency of CNNs for VBSLR, researchers have developed an Optimized Convolution Neural Network (OCNN) that incorporates optimization and regularization techniques. Optimization techniques such as gradient descent and adaptive learning rates help to improve the convergence speed and accuracy of the network, while regularization techniques such as dropout and weight decay help to prevent overfitting and improve generalization.
By fine-tuning the hyperparameters of the OCNN and leveraging advanced optimization and regularization techniques, researchers have been able to achieve state-of-the-art results in VBSLR tasks. The OCNN has demonstrated superior performance in recognizing complex sign language gestures and has shown robustness to noise and variations in input data.
Overall, the Optimized Convolution Neural Network (OCNN) represents a significant advancement in the field of VBSLR, offering a powerful and efficient solution for improving communication accessibility for individuals who are deaf or hard of hearing. With further research and development, the OCNN has the potential to revolutionize the way we interact and communicate with the deaf community.
#Optimized #Convolution #Neural #Network #OCNN #VBSLR #Optimized #Convolution #Neural #Network #OCNN #VoiceBased #Sign #Language #Recognition #Optimization #Regularization
Optical Character Recognition of Sanskrit Manuscripts using Convolution Neural Networks
Price: $57.98
(as of Dec 26,2024 16:29:42 UTC – Details)
Publisher : Eliva Press (November 26, 2024)
Language : English
Paperback : 157 pages
ISBN-10 : 9999321712
ISBN-13 : 978-9999321716
Item Weight : 10.4 ounces
Dimensions : 6 x 0.36 x 9 inches
Optical Character Recognition (OCR) technology has revolutionized the way we interact with written texts, allowing for quick and accurate conversion of printed or handwritten text into digital format. However, OCR systems are often limited in their ability to accurately recognize characters from languages with complex scripts, such as Sanskrit.Sanskrit is an ancient language that has been used in the Indian subcontinent for thousands of years, and its script, Devanagari, is known for its intricate and ornate characters. Recognizing these characters accurately can be a challenge for traditional OCR systems, which are typically trained on more common scripts like Latin or Cyrillic.
In recent years, researchers have been exploring the use of Convolutional Neural Networks (CNNs) for OCR of Sanskrit manuscripts. CNNs are a type of deep learning model that have shown great success in image recognition tasks, making them well-suited for the complex and detailed characters of Devanagari script.
By training CNNs on a large dataset of Sanskrit manuscripts, researchers have been able to create OCR systems that can accurately recognize and transcribe Sanskrit text with high precision. These systems have the potential to greatly accelerate the digitization of Sanskrit manuscripts, making these valuable historical texts more accessible to researchers and scholars around the world.
Overall, the use of CNNs for OCR of Sanskrit manuscripts represents an exciting development in the field of digital humanities, and holds great promise for preserving and disseminating this important cultural heritage.
#Optical #Character #Recognition #Sanskrit #Manuscripts #Convolution #Neural #Networks
Convolution Recurrent Neural Networks for Image Classification: Enhancing Visual Communication and Expression in Instant Messaging Platforms
Price: $48.00
(as of Dec 24,2024 18:04:24 UTC – Details)
Publisher : LAP LAMBERT Academic Publishing (July 17, 2023)
Language : English
Paperback : 72 pages
ISBN-10 : 6206738930
ISBN-13 : 978-6206738930
Item Weight : 4.4 ounces
Dimensions : 5.91 x 0.17 x 8.66 inches
Convolution Recurrent Neural Networks (CRNN) have been gaining popularity in recent years for their ability to effectively analyze sequential data, such as images, by combining the strengths of convolutional and recurrent neural networks. In the context of image classification, CRNNs have shown promising results in enhancing visual communication and expression in instant messaging platforms.One of the key advantages of CRNNs is their ability to capture both spatial and temporal information in images. By leveraging the convolutional layers to extract spatial features and the recurrent layers to capture temporal dependencies, CRNNs can effectively analyze the content of images and understand the context in which they are presented.
In the context of instant messaging platforms, where users often communicate through images, CRNNs can play a crucial role in enhancing the visual communication experience. By accurately classifying images based on their content, CRNNs can help users quickly find and share relevant images, express themselves more effectively, and engage in richer conversations.
Furthermore, CRNNs can also be used to automatically generate captions for images, providing additional context and enhancing the overall communication experience. This can be particularly useful in scenarios where users may not have the time or ability to type out detailed descriptions for their images.
Overall, Convolutional Recurrent Neural Networks hold great potential in revolutionizing image classification in instant messaging platforms, ultimately enhancing visual communication and expression for users. As this technology continues to evolve, we can expect to see even more exciting applications and improvements in the way we communicate through images.
#Convolution #Recurrent #Neural #Networks #Image #Classification #Enhancing #Visual #Communication #Expression #Instant #Messaging #Platforms
