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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.
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on (Volume:2 )
Date of Conference: 25-29 Oct. 1993