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.

A template-matching pandemonium recognizes unconstrained handwritten characters with high accuracy

Abstract: Psychological data suggest that internal representations such as mental images can be used as templates in visual pattern recognition. But computational studies suggest that traditional template matching is insufficient for high-accuracy recognition of real-life patterns such as handwritten characters. Here we explore a model for visual pattern recognition that combines a template-matching and a feature-analysis approach: Character classification is based on weighted evidence from a number of analyzers (demons), each of which computes the degree of match between the input character and a stored template (a copy of a previously presented character). The template-matching pandemonium was trained to recognize totally unconstrained handwritten digits. With a mean of 37 templates per type of digit, the system has attained a recognition rate of 95.3%, which falls short of human performance by only 2%–3%.

Network-based approach to online cursive script recognition

Abstract: The idea of combining the network of HMMs and the dynamic programming-based search is highly relevant to online handwriting recognition. The word model of HMM network can be systematically constructed by concatenating letter and ligature HMM's while sharing common ones. Character recognition in such a network can be defined as the task of best aligning a given input sequence to the best path in the network. One distinguishing feature of the approach is that letter segmentation is obtained simultaneously with recognition but no extra computation is required.

Handwritten digits recognition by a supervised Kohonen-like learning algorithm

Abstract: The authors describe the application of a supervised learning algorithm, based on Kohonen's self-organizing feature maps, to pattern recognition. They adopt an idea previously used for semantic map organization and discuss its adaptation to pattern recognition. The basic motivation is to organize the map by the patterns and their association targets simultaneously. A by-product of this process is that the class labeling of neurons on the map emerges during the learning phase. The algorithm and results obtained for a handwritten zip code database are presented. >

An HMM-based legal amount field OCR system for checks

Abstract: The system described in this paper applies hidden Markov technology to the task of recognizing the handwritten legal amount on personal checks. We argue that the most significant source of error in handwriting recognition is the segmentation process. In traditional handwriting OCR systems, recognition is performed at the character level, using the output of an independent segmentation step. Using a fixed stepsize series of vertical slices from the image, the HMM system described in this paper avoids taking segmentation decisions early in the recognition process.

A New Clustering Method for Improving Plasticity and Stability in Handwritten Character Recognition Systems

Abstract: This paper presents a new online clustering algorithm in order to improve plasticity and stability in handwritten character recognition systems. Our clustering algorithm is able to automatically determine the optimal number of clusters in the input data. An incremental learning technique similar to Adaptive Resonance Theory (ART) is used to determine the best cluster for new data. Our technique also allows the previously learned clusters to be merged whenever the newly arrived data points push their centers close together. We also developed new features and similarity measures in order to describe and compare the shapes of handwritten digits to be used in our clustering algorithm. Results of our algorithm on clustering the shapes of the handwritten numerals from the CENPARMI isolated digit database are shown. Our method can incrementally learn new handwriting styles of digits, without forgetting the previous ones, therefore it can improve plasticity and stability.