Speech Mentor for Visually Impaired People

Sahithi, P. G. L.; Bhavana, V.; ShushmaSri, K.; Jhansi, K.; Madhuri, Ch. Raga

doi:10.1007/978-981-16-9669-5_40

Cited by 1 publication

(1 citation statement)

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“…In [33], the authors presented the unified descriptor network, Dual Desc, that could outperform the NetVLAD architecture in terms of describing images. A wearable device validates real-world information, and the suggested visual localization suggestions employ multimodal images to avoid any issues associated with RGB photos.The author [34] developed a voice mentor system that reads content such as books, currency notes, and shopping parcels and provides audio output to the user. The Raspberry Pi is deployed in this approach to support the portable camera and audio signals through headphones.…”

Section: At Based On Raspberry Pimentioning

confidence: 99%

Deep Learning Reader for Visually Impaired

Ganesan¹,

Azar

Alsenan

et al. 2022

Electronics

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Recent advances in machine and deep learning algorithms and enhanced computational capabilities have revolutionized healthcare and medicine. Nowadays, research on assistive technology has benefited from such advances in creating visual substitution for visual impairment. Several obstacles exist for people with visual impairment in reading printed text which is normally substituted with a pattern-based display known as Braille. Over the past decade, more wearable and embedded assistive devices and solutions were created for people with visual impairment to facilitate the reading of texts. However, assistive tools for comprehending the embedded meaning in images or objects are still limited. In this paper, we present a Deep Learning approach for people with visual impairment that addresses the aforementioned issue with a voice-based form to represent and illustrate images embedded in printed texts. The proposed system is divided into three phases: collecting input images, extracting features for training the deep learning model, and evaluating performance. The proposed approach leverages deep learning algorithms; namely, Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), for extracting salient features, captioning images, and converting written text to speech. The Convolution Neural Network (CNN) is implemented for detecting features from the printed image and its associated caption. The Long Short-Term Memory (LSTM) network is used as a captioning tool to describe the detected text from images. The identified captions and detected text is converted into voice message to the user via Text-To-Speech API. The proposed CNN-LSTM model is investigated using various network architectures, namely, GoogleNet, AlexNet, ResNet, SqueezeNet, and VGG16. The empirical results conclude that the CNN-LSTM based training model with ResNet architecture achieved the highest prediction accuracy of an image caption of 83%.

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Section: At Based On Raspberry Pimentioning

confidence: 99%