Showing papers on "Signature recognition published in 1984"

Pattern Recognition and Computer Vision

Abstract: With more powerful algorithms and greater computing power, the once \"unreachable\" pattern recognition and computer vision problems can now be resolved, simplifying complex decisions about input data. 274 In the last 20 years, interest in pattern recognition and computer vision problems has increased dramatically. This interest has in turn created a need for theoretical methods and experimental software and hardware to aid the design of computer vision and pattern recognition systems. Over 25 books have been published on these topics as have a number of conference proceedings and special issues of journals. * Pattern recognition machines and computer vision systems have been designed and built for everything from character recognition , target detection, medical diagnosis , analysis of biomedical signals and images, remote sensing, and identification of human faces and fingerprints , to reliability, socioeconomics, archaeology, speech recognition and understanding, machine part recognition , and automatic inspection. 1,2 In this article, we briefly review the most recent developments in pattern recognition and computer vision. Many definitions of pattern recognition have been proposed. We view pattern recognition here as being concerned primarily with the description and analysis of measurements taken from physical or mental processes. Pattern recognition often begins with some kind of preprocessing to remove noise and redundancy in the measurements , thereby ensuring an effective and efficient pattern description. Next, a set of characteristic measurements , numerical and/or nonnumeri-cal, and relations among these measurements are extracted to represent patterns. Patterns are then analyzed (classified and/or described) on the basis of the representation. Naturally, we need a good set of characteristic measurements and a firm idea of how they interrelate in representing patterns so that patterns can be easily recognized. Knowledge of the statistical and structural characteristics of patterns is vital to achieving this goal and should be fully utilized. From this point of view, then, pattern recognition means analyzing pattern characteristics as well as designing recognition systems.

Segmentation in isolated word recognition using vector quantization

Abstract: Two types of isolated digit recognition systems based on vector quantization were tested in a speaker-independent task. In both types of systems, a digit was modelled as a sequence of codebooks generated from segments of training data. In systems of the first type, the training and unknown utterances were simply partitioned into 1, 2 or 3 equal-length segments. Recognition involved computing the distortion when the input spectra were vector quantized using the codebook sequences. These systems are closely related to recognizers proposed by Burton et al.[1]. In systems of the second type, training segments corresponded to acoustic-phonetic units and were obtained from hand-marked data. Recognition involved generating a minimum-distortion segmentation of the unknown by dynamic programming. Accuracies approaching 96-97% were achieved by both types of systems.

One-pass syntax-directed connected-word recognition in a time-sharing environment

Abstract: This paper describes a syntax-directed real-time connected-word recognition system and compares the performance of different recognition algorithms. This is a speaker dependent system based on whole word template matching, and the recognition is done using a one-pass dynamic time-warping algorithm. The symmetric local constraints of Sakoe and Chiba are used for connected word recognition, and with proper normalization of the accumulated distances, result in better performance than the Itakura local constraints. By allowing a noise template to start and end the sentences during the recognition process, most of the errors due to a bad endpoint detection are eliminated. When memory and computational resources are limited, vector quantization allows more templates for each word in the dictionary, and as a consequence, the recognition performance increases. By carefully implementing the algorithm on standard hardware (VAX 11/780 and FPS AP-120B), real-time recognition is achieved for vocabularies of up to 100 templates, or up to 250 templates if vector quantization is used.