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A car license plate recognition system (CLPR-system) has been developed to identib vehicles by the contents of their license plate for speed-limit enforcement. This type of application puts high demands on the reliability of the CLPR-system. A combination of neural andfizzy techniques is used to guarantee a very low error rate at an acceptable recognition rate. First experiments along highways in the Netherlands show that the system has an error rate of 0.02% at a recognition rate of 98.51%. These results are also compared with otherpublished CLPR-systems.

Software architecture can be seen as a decision making process; it involves making the right decisions at the right time. Typically, these design decisions are not explicitly represented in the artifacts describing the design. They reside in the minds of the designers and are therefore easily lost. Rationale management is often proposed as a solution, but lacks a close relationship with software architecture artifacts. Explicit modeling of design decisions in the software architecture bridges this gap, as it allows for a close integration of rationale management with software architecture. This improves the understandability of the software architecture. Consequently, the software architecture becomes easier to communicate, maintain and evolve. Furthermore, it allows for analysis, improvement, and reuse of design decisions in the design process.

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