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A lexicon driven approach to handwritten word recognition for real-time applications

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TLDR
Experimental results prove that the approach using the variable duration outperforms the method using fixed duration in terms of both accuracy and speed.
Abstract
A fast method of handwritten word recognition suitable for real time applications is presented in this paper. Preprocessing, segmentation and feature extraction are implemented using a chain code representation of the word contour. Dynamic matching between characters of a lexicon entry and segment(s) of the input word image is used to rank the lexicon entries in order of best match. Variable duration for each character is defined and used during the matching. Experimental results prove that our approach using the variable duration outperforms the method using fixed duration in terms of both accuracy and speed. Speed of the entire recognition process is about 200 msec on a single SPARC-10 platform and the recognition accuracy is 96.8 percent are achieved for lexicon size of 10, on a database of postal words captured at 212 dpi.

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Journal ArticleDOI

Online and off-line handwriting recognition: a comprehensive survey

TL;DR: The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.
Journal ArticleDOI

An overview of character recognition focused on off-line handwriting

TL;DR: The historical evolution of CR systems is presented, the available CR techniques, with their superiorities and weaknesses, are reviewed and directions for future research are suggested.
Journal ArticleDOI

Individuality of handwriting.

TL;DR: In this article, the authors used computer algorithms for extracting features from scanned images of handwriting, e.g., line separation, slant, character shapes, etc., to quantitatively establish individuality by using machine learning approaches.
Journal ArticleDOI

A survey on off-line Cursive Word recognition

TL;DR: This survey is divided into two parts, the first one dealing with the general aspects of Cursive Word Recognition, the second one focusing on the applications presented in the literature.
Journal ArticleDOI

Off-Line Arabic Character Recognition --- A Review

TL;DR: This review is organised into five major sections, covering a general overview, Arabic writing characteristics, Arabic text recognition system, Arabic OCR software and conclusions.
References
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Book

Pattern classification and scene analysis

TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
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Fundamentals of speech recognition

TL;DR: This book presents a meta-modelling framework for speech recognition that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually modeling speech.
Journal ArticleDOI

Computer Processing of Line-Drawing Images

TL;DR: Various forms of line drawing representation are described, different schemes of quantization are compared, and the manner in which a line drawing can be extracted from a tracing or a photographic image is reviewed.
Journal ArticleDOI

Off-line cursive script word recognition

TL;DR: In this paper, a word image is transformed through a hierarchy of representation levels: points, contours, features, letters, and words, and a unique feature representation is generated bottom-up from the image using statistical dependences between letters and features.
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