A BAR-CODE RECOGNITION SYSTEM USING BACKPROPAGATION NEURAL NETWORKS

In this paper, a bar-code recognition system using neural networks is proposed. It is well known that in many stores laser bar-code readers are used 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 the human operator has to manipulate either the sensor or the objects, or both. For the purpose of in-store automation, the human operator needs to be removed from the process, i.e. a robot with visual capability is required to play an important role in such a system. This paper proposes a camera-based bar-cone recognition system using backpropagation neural networks. The ultimate goal of this approach is to use a camera instead of a laser reader so that in-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, a trained backpropagation neural network is used to perform the bar-code recognition task. Experiments have been conducted to corroborate the efficiency of the proposed method.