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Journal ArticleDOI

Context in word recognition

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TLDR
Examination of several techniques for integrating an independent contextual postprocessor (CPP) into a full classification system finds that a standardized CPP can be built independently of the rest of the classification system.
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This article is published in Pattern Recognition.The article was published on 1976-01-01. It has received 50 citations till now. The article focuses on the topics: Word error rate & Word recognition.

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Citations
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Techniques for automatically correcting words in text

Karen Kukich
TL;DR: Research aimed at correcting words in text has focused on three progressively more difficult problems: nonword error detection; (2) isolated-word error correction; and (3) context-dependent work correction, which surveys documented findings on spelling error patterns.
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Random field models in image analysis

TL;DR: This review paper explains how Gibbs and Markov random field models provide a unifying theme for many contemporary problems in image analysis and allows the introduction of spatial context into pixel labeling problems, such as segmentation and restoration.
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Computer programs for detecting and correcting spelling errors

TL;DR: Peterson investigates the basic structure of several such existing programs and their approaches to solving the problems which arise when this type of program is created.
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n-Gram Statistics for Natural Language Understanding and Text Processing

TL;DR: The positional distributions of n-grams obtained in the present study are discussed and statistical studies on word length and trends ofn-gram frequencies versus vocabulary are presented.
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Automatic spelling correction in scientific and scholarly text

TL;DR: The SPEEDCOP program as discussed by the authors was developed as part of the Spelling Error Detection Project (Spelling Error. Detection/ ©1984ACM0001-0782/84/0400-0358 75¢ Correction Project).
References
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Journal ArticleDOI

A technique for computer detection and correction of spelling errors

TL;DR: The method described assumes that a word which cannot be found in a dictionary has at most one error, which might be a wrong, missing or extra letter or a single transposition.
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A Contextual Postprocessing System for Error Correction Using Binary n-Grams

TL;DR: Various algorithms utilizing context are considered, from a dictionary algorithm which has available the maximum amount of information, to a set of contextual algorithms utilizing positional binary n-gram statistics.
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Decision making in Markov chains applied to the problem of pattern recognition

TL;DR: The optimal decision rule is derived under the assumption of Markov dependence among the patterns to be recognized and applied to character-recognition problems.
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Experiments in the Contextual Recognition of Cursive Script

TL;DR: A structure for a contextual cursive script recognition system that makes use of letter context to determine word length, letter segmentation, and character identity to achieve contextual recognition at the word level is presented.
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Contextual Word Recognition Using Binary Digrams

TL;DR: The concept of binary digrams is introduced which overcomes some of the problems of past approaches and can be used to extract offectively the "syntax" of the dictionary while requiring very modest amounts of storage.