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Convolutional Neural Network Based Serial Number Recognition Method for Indian Rupee Banknotes

机译:基于卷积神经网络的印度卢比纸币序列号识别方法

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The recognition of the banknote serial number, which constitutes important data used for various purposes is one of the important functions of banknote counters. However, traditional character recognition methods are limited in terms of speed and performance of serial number recognition. Therefore, in this paper, we propose a character extraction method based on the aspect ratio of banknotes and a character recognition method based on a convolutional neural network (CNN). For character extraction, de-skewing was performed first. Then, the serial number was estimated on the basis of the aspect ratio of the banknote. Further, we designed four types of CNN-based neural networks for character recognition and adopted the most appropriate neural network. Subsequently, we confirmed that the average recognition performance per character for each neural network was 99.85%.
机译:构成用于各种目的的重要数据的钞票序列号的识别是钞票计数器的重要功能之一。但是,传统的字符识别方法在序列号识别的速度和性能方面受到限制。因此,本文提出了一种基于钞票长宽比的字符提取方法和一种基于卷积神经网络(CNN)的字符识别方法。对于字符提取,首先执行去歪斜。然后,基于钞票的纵横比来估计序列号。此外,我们设计了四种基于CNN的神经网络进行字符识别,并采用了最合适的神经网络。随后,我们确认每个字符对每个神经网络的平均识别性能为99.85%。

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