Showing papers on "Intelligent word recognition published in 1970"

Word recognition by means of orthogonal functions

Abstract: This paper describes experiments in which word recognition is based on comparing the projections of input words on an orthogonal basis with those of a stored library of words. An initial orthogonal basis is determined from the generalized spectrum of short time segments selected from a vocabulary of ten words. The initial basis is optimized by minimizing the complementary error energy. By projecting a spoken word onto the optimum orthogonal basis, a sequence of numbers is generated to represent the word. By correlating the absolute values of the sequence with those of a stored library of words, the spoken word is identified. The percent of correct recognition varies from 71.6 to 96.6 percent for two speakers. Techniques are developed to improve the recognition scores and to reduce the lengthy computer processing time and large storage requirement. First a master template is made for each word by averaging six templates for the particular word. For one speaker the percent of correct recognition increases to 100 percent when incoming words are compared against the master templates. For a second speaker, the recognition rates improve significantly and vary between 93 and 98 percent when the master templates are used. To further improve the recognition process, the feasibility of grouping words into several classes is demonstrated. The classifications are based on the locations of formant regions and the time durations of each spoken word.

Recognition of Handprinted Numerals by Two-Stage Feature Extraction

Abstract: An optical character recognition system for handprinted numerals of noisy and low-resolution measurement is proposed. The system consists of the two-stage feature extraction process. In the first stage a set of primary features insensitive to the quality and format of a black-white bit pattern are extracted. In the second stage, a set of properties capable of discriminating the character classes is derived from primary features. The system is simple and reliable in that only three kinds of primary features are needed to be detected. The recognition is based on the decision tree which tests the logic statements of secondary features.

Learning algorithms for multi-class pattern classification and problems associated with on-line handwritten character recognition

Abstract: On-line handwritten alphanumeric character recognition system and learning algorithm for multiclass pattern classification