Automatic banknote sheet cut-and-bundle machines are widely used within the scope of banknote production. Beside
the cutting-and-bundling, which is a mature technology, image-processing-based quality inspection for this type of
machine is attractive. We present in this work a new real-time Touchless Counting and perspective cutting blade quality
insurance system, based on a Color-CCD-Camera and a dual-core Computer, for cut-and-bundle applications in
banknote production. The system, which applies Wavelet-based multi-scale filtering is able to count banknotes inside a
100-bundle within 200-300 ms depending on the window size.
Automatic sheet inspection in banknote production has been used as a standard quality control tool for more than a
decade. As more and more print techniques and new security features are established, total quality in bank note printing
must be guaranteed. This aspect has a direct impact on the research and development for bank note inspection systems
in general in the sense of technological sustainability. It is accepted, that print defects are generated not only by printing
parameter changes, but also by mechanical machine parameter changes, which will change unnoticed in production.
Therefore, a new concept for a multi-sensory adaptive learning and classification model based on Fuzzy-Pattern-
Classifiers for data inspection and machine conditioning is proposed. A general aim is to improve the known inspection
techniques and propose an inspection methodology that can ensure a comprehensive quality control of the printed
substrates processed by printing presses, especially printing presses which are designed to process substrates used in the
course of the production of banknotes, security documents and others. Therefore, the research and development work in
this area necessitates a change in concept for banknote inspection in general. In this paper a new generation of FPGA
(Field Programmable Gate Array) based real time inspection technology is presented, which allows not only colour
inspection on banknote sheets, but has also the implementation flexibility for various inspection algorithms for security
features, such as window threads, embedded threads, OVDs, watermarks, screen printing etc., and multi-sensory data
processing. A variety of algorithms is described in the paper, which are designed for and implemented on FPGAs. The
focus is based on algorithmic approaches.
The authenticity checking and inspection of bank notes is a high labour intensive process where traditionally every note on every sheet is inspected manually. However with the advent of more and more sophisticated security features, both visible and invisible, and the requirement of cost reduction in the printing process, it is clear that automation is required. As more and more print techniques and new security features will be established, total quality security, authenticity and bank note printing must be assured. Therefore, this factor necessitates amplification of a sensorial concept in general.
We propose a concept for both authenticity checking and inspection methods for pattern recognition and classification for securities and banknotes, which is based on the concept of sensor fusion and fuzzy interpretation of data measures. In the approach different methods of authenticity analysis and print flaw detection are combined, which can be used for vending or sorting machines, as well as for printing machines. Usually only the existence or appearance of colours and
their textures are checked by cameras. Our method combines the visible camera images with IR-spectral sensitive sensors, acoustical and other measurements like temperature and pressure of printing machines.
Nonlinear spatial transforms and fuzzy pattern classification with unimodal potential functions are already established in signal processing. They have proved to be excellent tools in feature extraction and classification. We propose an inspection method for pattern recognition and classification of two dimensional translation variant security elements such as stripes, kinegrams and others, which are widely used as applications in bank note printing.
The system is based on discrete non linear translation invariant circular transforms and fuzzy pattern classification. Nonlinear discrete circular transforms are adaptable transforms, which can be optimized for different application tasks, such as translation variant object analysis and position location. They are mainly used as generators for feature vectors. Even though, the feature vector is theoretically translation invariant, the object movement creates a translation tolerant feature vector, because in real systems and applications many problems can occur, such as signal and
optical distortions. Therefore, the features should be further analysed by a fuzzy pattern classifier. Implementation of
the transforms and fuzzy pattern classifier in radix-2-structures is possible, allowing fast calculations with a computational
complexity of O(N) up to O(Nld(N)). Furthermore, the algorithms can be implemented in one Field Programmable Gate Array (FPGA), which operates with 40 MHz clock rate.