Our idea of setting a traffic light is as follows. Suppose there are a number of cars with their destination address standing before a crossing. All cars communicate to the traffic light their specific place in the queue and their destination address. Now the traffic light has to decide which option (ie, which lanes are to be put on green) is optimal to minimize the long-term average waiting time until all cars have arrived at their destination address. The learning traffic light controllers solve this problem by estimating how long it would take for a car to arrive at its destination address (for which the car may need to pass many different traffic lights) when currently the light would be put on green, and how long it would take if the light would be put on red.

We developed a system for finding address blocks on mail pieces that can process four images per second. Besides locating the address block, our system also determines the writing style, handwritten or machine printed, and moreover, it measures the skew angle of the text lines and cleans noisy images. A layout analysis of all the elements present in the image is performed in order to distinguish drawings and dirt from text and to separate text of advertisement from that of the destination address. A speed of more than four images per second is obtained on a modular hardware platform, containing a board with two of the NET32K neural net chips, a SP ARC2 processor board, and a board with 2 digital signal processors. The system has been tested with more than 100,000 images. Its performance depends on the quality of the images, and lies between 85% correct location in very noisy images to over 98% in cleaner images.

We developed a system for finding address blocks on mail pieces that can process four images per second. Besides locating the address block, our system also determines the writing style, handwritten or machine printed, and moreover, it measures the skew angle of the text lines and cleans noisy images. A layout analysis of all the elements present in the image is performed in order to distinguish drawings and dirt from text and to separate text of advertisement from that of the destination address. A speed of more than four images per second is obtained on a modular hardware platform, containing a board with two of the NET32K neural net chips, a SP ARC2 processor board, and a board with 2 digital signal processors. The system has been tested with more than 100,000 images. Its performance depends on the quality of the images, and lies between 85% correct location in very noisy images to over 98% in cleaner images.

We developed a system for finding address blocks on mail pieces that can process four images per second. Besides locating the address block, our system also determines the writing style, handwritten or machine printed, and moreover, it measures the skew angle of the text lines and cleans noisy images. A layout analysis of all the elements present in the image is performed in order to distinguish drawings and dirt from text and to separate text of advertisement from that of the destination address. A speed of more than four images per second is obtained on a modular hardware platform, containing a board with two of the NET32K neural net chips, a SPARC2 processor board, and a board with 2 digital signal processors. The system has been tested with more than 100,000 images. Its performance depends on the quality of the images, and lies between 85% correct location in very noisy images to over 98% in cleaner images.

An important task in postal automation technology is determining the position and orientation of the destination address block in the image of a mail piece such as a letter, magazine, or parcel. The corresponding subimage is then presented to a human operator or a machine reader (optical character reader) that can read the zip code and, if necessary, other address information and direct the mail piece to the appropriate sorting bin. Analysis of physical characteristics of mail pieces indicates that in order to automate the address finding task, several different image analysis operations are necessary. Some examples are locating a rectangular white address label on a multicolor background, progressively grouping characters into text lines and text lines into text blocks, eliminating candidate regions by specialized detectors (for example, detecting regions such as postage stamps), and identifying handwritten regions. Described here are several operations, their utility as predicted by statistics of mail piece characteristics, and the results of applying the operations to a task set of mail piece images. A problem-solving architecture based on the blackboard model of problem solving for appropriately invoking the tools and combining their results is described.