Abstract
An algorithm for recognizing banknotes is required in many fields, such as banknote-counting machines and automatic teller machines (ATM). Due to the size and cost limitations of banknote-counting machines and ATMs, the banknote image is usually captured by a one-dimensional (line) sensor instead of a conventional two-dimensional (area) sensor. Because the banknote image is captured by the line sensor while it is moved at fast speed through the rollers inside the banknote-counting machine or ATM, misalignment, geometric distortion, and non-uniform illumination of the captured images frequently occur, which degrades the banknote recognition accuracy. To overcome these problems, we propose a new method for recognizing banknotes. The experimental results using two-fold cross-validation for 61,240 United States dollar (USD) images show that the pre-classification error rate is 0%, and the average error rate for the final recognition of the USD banknotes is 0.114%.
Highlights
Functionality for detecting counterfeit banknotes and recognizing banknotes are required in various machines, such as banknote-counting machines and automatic teller machines (ATMs)
(2) In order to further reduce this effect on the recognition accuracy, we propose the feature extraction method by principal component analysis (PCA) with the sub-sampled image
We collected a database consisting of 61,240 United States dollar (USD) banknote images for our experiments
Summary
Functionality for detecting counterfeit banknotes and recognizing banknotes are required in various machines, such as banknote-counting machines and automatic teller machines (ATMs). Banknote recognition is defined as the recognition of the type of banknote (e.g., $1, $2, and $5), the direction of the banknote, and the date of issue of the banknote (e.g., least recent, recent, and most recent). Banknote recognition facilitates the detection of counterfeit banknotes and allows the condition of banknotes to be monitored. A banknote is constituted by four different patterns (obverse-forward, obverse-backward, reverse-forward, and reverse-backward) according to the direction of the banknote received by the machine. This increases the number of banknote classes four-fold. The complexity of banknote recognition increases which, in turn, increases the processing time and the banknote recognition error rate
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