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Many methods have been proposed for character recognition but they are ojlen subjected to substantial constraints, due to unexpected d@culties encountered in real-li&e images. A real-lge image may be complex for a varieiy of reasons. Rust, m d , peeling paint, or fading color may distort the images of the characters: uneven lighting may make them di9cult to discern. This paper presents the ECON (vehicle and Container Number Recognition) system, which takes into account a wide range of real-life consi&rations and aims at oflering applicable solutions to some industries. Ajler being tested under outdoor environment and 24-hour operations, the proposed methods proved to have accuracy higher than 95%. f i e system was commercially employed in car parks, bus stations, border checkpoints, container terminals and container depots.

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