Abstract: A visual bar code recognition method which combines conventional decoding techniques with optical character recognition (OCR). The visual bar code recognition method captures an image of an object containing a bar code. Regardless of the orientation of the bar code within the field-of-view, the system detects the presence of the bar code, and decodes it using the bar/space patterns. It then produces an independent decoding of the human-readable numbers printed on the bar code using OCR. From these two decodings, it determines the identity of the object. It verifies this identity by comparing the physical characteristics of the object from the image with the known features of the product.

Abstract: An item recognition system and method which is particularly suited for automating entry of items too small to carry readable bar code labels. The system includes a camera which digitizes the image to produce a digitized image and a gray-scale digitized image. A binary image of the gray-scale image is then produced from which the computer records an image of the item, and a computer coupled to the camera which digitizes the image to produce a digitized image and a gray-scale digitized image. A binary image of the gray-scale image is then produced from which the computer identifies the item from the binary image and obtains the price from a price-lookup file.

Abstract: An adaptive segmentation system that utilizes a genetic algorithm in image segmentation. The system incorporates a closed-loop feedback mechanism in the segmentation/learning cycle. The system can adapt to changes appearing in the images being segmented, caused by variations of such factors as time and weather. Adaptation is achieved with a measure based on differences of analyzed past imagery and current imagery and on the criteria for segmentation quality. The invention is not dependent on any particular segmentation algorithm or specific sensor type.