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A High Performance Banknote Recognition System Based on a One-Dimensional Visible Light Line Sensor.

Park YH, Kwon SY, Pham TD, Park KR, Jeong DS, Yoon S - Sensors (Basel) (2015)

Bottom Line: 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%.

View Article: PubMed Central - PubMed

Affiliation: Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Korea. fdsarew@hanafos.com.

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%.

No MeSH data available.


Related in: MedlinePlus

Classification accuracy of LDA according to number of LDA dimensions: (a) Group 1; (b) Group 2.
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sensors-15-14093-f006: Classification accuracy of LDA according to number of LDA dimensions: (a) Group 1; (b) Group 2.

Mentions: For comparison, we also measured the classification accuracy using linear-discriminant analysis (LDA) instead of PCA. The classification results from using the training data are shown in Figure 6.


A High Performance Banknote Recognition System Based on a One-Dimensional Visible Light Line Sensor.

Park YH, Kwon SY, Pham TD, Park KR, Jeong DS, Yoon S - Sensors (Basel) (2015)

Classification accuracy of LDA according to number of LDA dimensions: (a) Group 1; (b) Group 2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507579&req=5

sensors-15-14093-f006: Classification accuracy of LDA according to number of LDA dimensions: (a) Group 1; (b) Group 2.
Mentions: For comparison, we also measured the classification accuracy using linear-discriminant analysis (LDA) instead of PCA. The classification results from using the training data are shown in Figure 6.

Bottom Line: 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%.

View Article: PubMed Central - PubMed

Affiliation: Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Korea. fdsarew@hanafos.com.

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%.

No MeSH data available.


Related in: MedlinePlus