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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44239

Title: Camera-based Bar Code Recognition System Using Neural Net
Authors: Shu-Jen Li
Hong-Yuan Liao
Liang-Hua Chen
Hsiao-Rong Tyna
Jun-Wei Hsieh
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Neural networks
Robotics and automation
Storage automation
Humans
Backpropagation
Counting circuits
Robot sensing systems
Robot vision systems
Cameras
Image segmentation
Date: 1993-10
Issue Date: 2017-11-16T03:33:23Z
Publisher: International Joint Conf. On Neural Networks
Abstract: Abstract:In this paper, a bar code recognition system using neural networks is proposed. It is well known that in many stores the laser bar code reader is adopted at check-out counters. However, there is a major constraint when this tool is used. That is, unlike traditional camera-based picturing, the distance between the laser reader (sensor) and the target object is close to zero when the reader is applied. This may result in inconvenience in store automation because human operator has to take care of either the sensor or the objects (or both). For the purpose of store automation, human operator has to be removed from the process, i.e., a robot with visual capability requires to play an important role in such system. In this paper, we propose a camera-based bar code recognition system using backpropagation neural networks. The ultimate goal of this approach is to use camera instead of laser reader such that store automation can be achieved. There are a number of steps involved in the proposed system. The first step the system has to perform is to locate the position and orientation of the bar code in the acquired image. Secondly, the proposed system has to segment the bar code. Finally, we use a trained backpropagation neural network to perform bar code recognition task. Experiments have been conducted to corroborate the proposed method.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44239
Appears in Collections:[資訊工程學系] 演講及研討會

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