Currency Serial Number Recognition
Date Issued
2015
Date
2015
Author(s)
Lu, Yi
Abstract
We propose an application-oriented Optical Character Recognition (OCR) method for Currency Serial Number Recognition (CSNR) in this thesis. The corresponding solution based on statistical feature and linear classifier was proposed for this problem. Our proposed system could achieve the accuracy of 99.5% per bill and the speed of 800 bills per minute in the banknote counting machine with low computational power of ARM9 at 300MHz. We also apply three advanced machine learning methods including Sparse Representation (SR), Matrix Factorization (MF), Gradient Boosting Decision Tree (GBDT) for this specific OCR problem. The high recognition capacities of these methods for OCR problem are confirmed. The experiment results in CSNR have shown these methods promising candidates for more general OCR problem. The visualization of currency serial number data revealed the implicit low-dimensional structure of data that is also observed by the analytical results of MF and SR methods.
Subjects
Optical Character Recognition
Currency Serial Number Recognition
Banknote Counting Machine
Matrix Factorization
Gradient Boosting Decision Tree
Sparse Representation
Data Visualization
Type
thesis
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