Intelligent word recognition

About: Intelligent word recognition is a research topic. Over the lifetime, 2480 publications have been published within this topic receiving 45813 citations.

Recognition of Handwritten Characters by Topological Feature Extraction and Multilevel Categorization

Abstract: A handwritten character recognition system has been designed by making use of topological feature extraction and multilevel decision making. By properly specifying a set of easily detectable topological features, it is possible to convert automatically the handwritten characters into stylized forms and to classify them into primary classes with similar topological configurations. Final recognition is accomplished by a secondary stage that performs local analysis on the characters in each primary category. The recognition system consists of two stages: global recognition, followed by local recognition. Automatic character stylization results in pattern clustering which simplifies the classification tasks considerably, while allowing a high degree of generality in the acceptable writing format. Simulation of this scheme on a digital computer has shown only 6 percent misrecognition.

A Front-End OCR for Omni-Font Persian/Arabic Cursive Printed Documents

Abstract: Compared to non-cursive scripts, optical character recognition of cursive documents comprises extra challenges in layout analysis as well as recognition of the printed scripts. This paper presents a front-end OCR for Persian/Arabic cursive documents, which utilizes an adaptive layout analysis system in addition to a combined MLP-SVM recognition process. The implementation results on a comprehensive database show a high degree of accuracy which meets the requirements of commercial use.

An Efficient Fuzzy Method for Handwritten Character Recognition

Abstract: The main challenge in handwritten character recognition involves the development of a method that can generate descriptions of the handwritten objects in a short period of time. Due to its low computational requirement, fuzzy logic is probably the most efficient method available for on-line character recognition. The most tedious task associated with using fuzzy logic for online character recognition is the building of the rule-base that would describe the characters to be recognized. The problem is complicated as different people write the same character in complete different ways. This paper describes a method that can be used to generate a fuzzy value database that describes the characters written by different individuals.

Handwritten Character Recognition using Perceptual Fuzzy-Zoning and Class Modular Neural Networks

Abstract: This paper present a novel feature extraction method for offline recognition of segmented handwritten characters based on the fuzzy-zoning and normalized vector distance measures. Experiments are conducted on forty four basic Malayalam handwritten characters. In the recognition experiments are conducted using class modular neural network with the proposed features and this method is found to be promising.

Voice Recognition Device and Method, and Program

Abstract: A speech recognition system in which a user may correct a recognition error resulting from speech recognition more efficiently and easily. Speech recognition means compares a plurality of words inputted from speech input means with a plurality of words stored in dictionary means, respectively, and determines a most-competitive word candidate. Word correction means has a word correction function of correcting the words constituting a word sequence displayed on a screen. Competitive word display commanding means selects one or more competitive words having competitive probabilities close to the competitive probability of the most-competitive word candidate and displays the one or more competitive words adjacent to the most-competitive word candidate. Competitive word selection means selects an appropriate correction word from the one or more competitive words. Word replacement commanding means causes one of the most-competitive word candidate to be replaced with the correction word selected by the competitive word selection means.