Expiry-Date Recognition System Using Combination of Deep Neural Networks for Visually Impaired

2020 ◽  
pp. 510-516
Author(s):  
Megumi Ashino ◽  
Yoshinori Takeuchi
Keyword(s):  
Neural Networks ◽  
Visually Impaired ◽  
Deep Neural Networks ◽  
Recognition System ◽  
Expiry Date

scholarly journals Expiry date recognition using deep neural networks

2020 ◽  
Vol 13 (1) ◽  
pp. 1-17
Author(s):  
Traian Rebedea ◽  
Vlad Florea
Keyword(s):  
Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Speech Synthesis ◽  
Efficient Solution ◽  
Deep Neural Networks ◽  
Accuracy Improvement ◽  
Expiry Date ◽  
Food Items ◽  
Optical Character

This paper proposes a deep learning solution for optical character recognition, specifically tuned to detect expiration dates that are printed on the packaging of food items. This method can be used to reduce food waste, having a significant impact on the design of smart refrigerators and can prove especially useful for persons with vision difficulties, by combining it with a speech synthesis engine. The main problem in designing an efficient solution for expiry date recognition is the lack of a large enough dataset to train deep neural networks. To tackle this issue, we propose to use an additional dataset composed of synthetically generated images. Both the synthetic and real image datasets are detailed in the paper and we show that the proposed method offers a 9.4% accuracy improvement over using real images alone.

scholarly journals Braille character detection using deep neural networks for an educational robot for visually impaired people

2020 ◽  
Author(s):  
Diego Gonçalves ◽  
Gabriel Santos ◽  
Márcia Campos ◽  
Alexandre Amory ◽  
Isabel Manssour
Keyword(s):  
Neural Networks ◽  
Visually Impaired ◽  
Deep Neural Networks ◽  
Ethylene Vinyl Acetate ◽  
Detection Methods ◽  
Current State ◽  
Visually Impaired People ◽  
Braille Character ◽  
Educational Robot ◽  
Public Dataset

Teaching computer programming to the visually impaired is a difficult task that has sparked a great deal of interest, in part due to its specific demands. Robotics has been one of the strategies adopted to help in this task. One system that uses robotics to teach programming for the visually impaired, called Donnie, has as its key part the need to detect Braille characters in a scaled-down environment. In this paper, we investigate the current state-of-the-art in Braille letter detection based on deep neural networks. For such, we provide a novel public dataset with 2818 labeled images of Braille characters, classified in the letters of the alphabet, and we present a comparison among some recent detection methods. As a result, the proposed Braille letters detection method could be used to assist in teaching programming for blind students using a scaled-down physical environment. The proposal of EVA (Ethylene Vinyl Acetate) pieces with pins to represent Braille letters in this environment is also a contribution.

USAGE OF ADVERSARIAL EXAMPLES TO PROTECT A HUMAN IMAGE FROM BEING DETECTED BY RECOGNITION SYSTEMS BASED ON DEEP NEURAL NETWORKS

2020 ◽  
pp. 32-38
Author(s):  
S. A. Sakulin ◽  
A. N. Alfimtsev ◽  
D. A. Loktev ◽  
A. O. Kovalenko ◽  
V. V. Devyatkov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
High Efficiency ◽  
Recognition System ◽  
Human Observer ◽  
Adversarial Examples ◽  
Recognition Systems ◽  
Human Image

Recently, human recognition systems based on deep machine learning, in particular, on the basis of deep neural networks, have become widespread. In this regard, research has become relevant in the field of protection against recognition by such systems. In this article a method of designing a specially selected type of camouflage applied to clothing, which will protect a person both from recognition by a human observer and from a deep neural network recognition system is proposed. This type of camouflage is constructed on the basis of competitive examples that are generated by a deep neural network. The article describes experiments on human protection from recognition by Faster-RCNN (Regional Convolution Neural Networks) Inception V2 and Faster-RCNN ResNet101 systems. However, the implementation of camouflage is considered on a macro level, which assesses the combination of the camouflage and background, and the micro level which analyzes the relationship between the properties of individual regions of the camouflage properties of the adjacent regions, with constraints on their continuity, smoothness, closure, asymmetry. The dependence of camouflage characteristics on the conditions of observation of the object and the environment is also considered: the transparency of the atmosphere, the intensity of pixels of the sky horizon and the background, the level of contrast of the background and the camouflaged object, the distance to the object. As an example of a possible attack, a “black box” attack, which involves preliminary testing of generated adversarial examples on a target recognition system without knowledge of the internal structure of this system, is considered. Results of these experiments showed the high efficiency of the proposed method in the virtual world, when there is access to each pixel of the image supplied to the input systems. In the real world, results are less impressive, which can be explained by the distortion of colors when printing on the fabric, as well as the lack of spatial resolution of this print.

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