Application on character recognition system on road sign for visually impaired: case study approach and future

Kim, Jaejoon

doi:10.11591/ijece.v10i1.pp778-785

Cited by 9 publications

(8 citation statements)

References 13 publications

(15 reference statements)

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“…The important questions that we must consider are how to use this feature and how to convey the important information only, rather than all of the information detected in the surrounding area [ 104 ]. The blob is a collection of pixels whose intensity is different from the other nearby pixels.…”

Section: Related Workmentioning

confidence: 99%

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

Busaeed

Katib

Albeshri

et al. 2022

Sensors

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Over a billion people around the world are disabled, among whom 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, and poor environments and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required in order to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach using a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on the smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image-processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system’s hardware and software design, having constructed their prototype implementations and tested them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than USD 80. Essentially, we provide designs of an inexpensive, miniature green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach enables faster inference and decision-making using relatively low energy with smaller data sizes, as well as faster communications for edge, fog, and cloud computing.

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Section: Related Workmentioning

confidence: 99%

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

Busaeed

Katib

Albeshri

et al. 2022

Sensors

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show abstract

“…One of the important things that we must consider is how to use this feature, and how to convey the important information only not all of the information detected around [104]. The blob is a collection of pixels whose intensity is different from the other nearby pixels.…”

Section: Text Recognitionmentioning

confidence: 99%

“…The blob is a collection of pixels whose intensity is different from the other nearby pixels. Although the MSER (Maximally Stable External Region) can detect the blob faster, SWT (Stroke Width Transform) algorithm can detect characters from an image with no separate learning process [104]. Shilkrot et al [105] developed FingerReader, a supporting reading system for visually impaired people to assist impaired persons in reading printed texts with a real-time response.…”

Section: Text Recognitionmentioning

confidence: 99%

LidSonic V2.0: LiDAR and Deep Learning-based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

Busaeed¹,

Katib²,

Albeshri³

et al. 2022

Preprint

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Over a billion people around the world are disabled, among them, 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, poor environment, and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach in a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, construct their prototype implementations, and test them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than $80. Essentially, we provide designs of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach affords faster inference and decision-making using relatively low energy with smaller data sizes as well as faster communications for the edge, fog, and cloud computing.

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“…Arakeri et al proposed a raspberry pi with NoIR camera to captures the readable material around the visually impaired and used a speech synthesis to generate sound in regional language [19]. Fink and Humayun [20], and more researcher [21,22] presented the invention for the visually impaired. A digital camera mounted on the person's eye or head is used to take snapshots of an image on demand and provided to an image processing algorithm.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people

Rachburee

Punlumjeak

2021

IJECE

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The World Health Organization (WHO) reported in 2019 that at least 2.2 billion people were visual-impairment or blindness. The main problem of living for visually impaired people have been facing difficulties in moving even indoor or outdoor situations. Therefore, their lives are not safe and harmful. In this paper, we proposed an assistive application model based on deep learning: YOLOv3 with a Darknet-53 base network for visually impaired people on a smartphone. The Pascal VOC2007 and Pascal VOC2012 were used for the training set and used Pascal VOC2007 test set for validation. The assistive model was installed on a smartphone with an eSpeak synthesizer which generates the audio output to the user. The experimental result showed a high speed and also high detection accuracy. The proposed application with the help of technology will be an effective way to assist visually impaired people to interact with the surrounding environment in their daily life.

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Application on character recognition system on road sign for visually impaired: case study approach and future

Cited by 9 publications

References 13 publications

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

LidSonic V2.0: LiDAR and Deep Learning-based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people

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