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OCR Context-Sensitive Error Correction Based on Google Web 1T 5-Gram Data Set  [PDF]
Youssef Bassil,Mohammad Alwani
Computer Science , 2012,
Abstract: Since the dawn of the computing era, information has been represented digitally so that it can be processed by electronic computers. Paper books and documents were abundant and widely being published at that time; and hence, there was a need to convert them into digital format. OCR, short for Optical Character Recognition was conceived to translate paper-based books into digital e-books. Regrettably, OCR systems are still erroneous and inaccurate as they produce misspellings in the recognized text, especially when the source document is of low printing quality. This paper proposes a post-processing OCR context-sensitive error correction method for detecting and correcting non-word and real-word OCR errors. The cornerstone of this proposed approach is the use of Google Web 1T 5-gram data set as a dictionary of words to spell-check OCR text. The Google data set incorporates a very large vocabulary and word statistics entirely reaped from the Internet, making it a reliable source to perform dictionary-based error correction. The core of the proposed solution is a combination of three algorithms: The error detection, candidate spellings generator, and error correction algorithms, which all exploit information extracted from Google Web 1T 5-gram data set. Experiments conducted on scanned images written in different languages showed a substantial improvement in the OCR error correction rate. As future developments, the proposed algorithm is to be parallelised so as to support parallel and distributed computing architectures.
OCR Post-Processing Error Correction Algorithm Using Google's Online Spelling Suggestion  [PDF]
Youssef Bassil,Mohammad Alwani
Journal of Emerging Trends in Computing and Information Sciences , 2012,
Abstract: With the advent of digital optical scanners, a lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into an electronic version that can be manipulated by a computer. For this purpose, OCR, short for Optical Character Recognition was developed to translate scanned graphical text into editable computer text. Unfortunately, OCR is still imperfect as it occasionally mis-recognizes letters and falsely identifies scanned text, leading to misspellings and linguistics errors in the OCR output text. This paper proposes a post-processing context-based error correction algorithm for detecting and correcting OCR non-word and real-word errors. The proposed algorithm is based on Google’s online spelling suggestion which harnesses an internal database containing a huge collection of terms and word sequences gathered from all over the web, convenient to suggest possible replacements for words that have been misspelled during the OCR process. Experiments carried out revealed a significant improvement in OCR error correction rate. Future research can improve upon the proposed algorithm so much so that it can be parallelized and executed overmultiprocessing platforms.
OCR Post-Processing Error Correction Algorithm using Google Online Spelling Suggestion  [PDF]
Youssef Bassil,Mohammad Alwani
Computer Science , 2012,
Abstract: With the advent of digital optical scanners, a lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into an electronic version that can be manipulated by a computer. For this purpose, OCR, short for Optical Character Recognition was developed to translate scanned graphical text into editable computer text. Unfortunately, OCR is still imperfect as it occasionally mis-recognizes letters and falsely identifies scanned text, leading to misspellings and linguistics errors in the OCR output text. This paper proposes a post-processing context-based error correction algorithm for detecting and correcting OCR non-word and real-word errors. The proposed algorithm is based on Google's online spelling suggestion which harnesses an internal database containing a huge collection of terms and word sequences gathered from all over the web, convenient to suggest possible replacements for words that have been misspelled during the OCR process. Experiments carried out revealed a significant improvement in OCR error correction rate. Future research can improve upon the proposed algorithm so much so that it can be parallelized and executed over multiprocessing platforms.
Applying Winnow to Context-Sensitive Spelling Correction  [PDF]
Andrew R. Golding,Dan Roth
Computer Science , 1996,
Abstract: Multiplicative weight-updating algorithms such as Winnow have been studied extensively in the COLT literature, but only recently have people started to use them in applications. In this paper, we apply a Winnow-based algorithm to a task in natural language: context-sensitive spelling correction. This is the task of fixing spelling errors that happen to result in valid words, such as substituting {\it to\/} for {\it too}, {\it casual\/} for {\it causal}, and so on. Previous approaches to this problem have been statistics-based; we compare Winnow to one of the more successful such approaches, which uses Bayesian classifiers. We find that: (1)~When the standard (heavily-pruned) set of features is used to describe problem instances, Winnow performs comparably to the Bayesian method; (2)~When the full (unpruned) set of features is used, Winnow is able to exploit the new features and convincingly outperform Bayes; and (3)~When a test set is encountered that is dissimilar to the training set, Winnow is better than Bayes at adapting to the unfamiliar test set, using a strategy we will present for combining learning on the training set with unsupervised learning on the (noisy) test set.
ASR Context-Sensitive Error Correction Based on Microsoft N-Gram Dataset  [PDF]
Youssef Bassil,Paul Semaan
Computer Science , 2012,
Abstract: At the present time, computers are employed to solve complex tasks and problems ranging from simple calculations to intensive digital image processing and intricate algorithmic optimization problems to computationally-demanding weather forecasting problems. ASR short for Automatic Speech Recognition is yet another type of computational problem whose purpose is to recognize human spoken speech and convert it into text that can be processed by a computer. Despite that ASR has many versatile and pervasive real-world applications,it is still relatively erroneous and not perfectly solved as it is prone to produce spelling errors in the recognized text, especially if the ASR system is operating in a noisy environment, its vocabulary size is limited, and its input speech is of bad or low quality. This paper proposes a post-editing ASR error correction method based on MicrosoftN-Gram dataset for detecting and correcting spelling errors generated by ASR systems. The proposed method comprises an error detection algorithm for detecting word errors; a candidate corrections generation algorithm for generating correction suggestions for the detected word errors; and a context-sensitive error correction algorithm for selecting the best candidate for correction. The virtue of using the Microsoft N-Gram dataset is that it contains real-world data and word sequences extracted from the web which canmimica comprehensive dictionary of words having a large and all-inclusive vocabulary. Experiments conducted on numerous speeches, performed by different speakers, showed a remarkable reduction in ASR errors. Future research can improve upon the proposed algorithm so much so that it can be parallelized to take advantage of multiprocessor and distributed systems.
A Winnow-Based Approach to Context-Sensitive Spelling Correction  [PDF]
Andrew R. Golding,Dan Roth
Computer Science , 1998,
Abstract: A large class of machine-learning problems in natural language require the characterization of linguistic context. Two characteristic properties of such problems are that their feature space is of very high dimensionality, and their target concepts refer to only a small subset of the features in the space. Under such conditions, multiplicative weight-update algorithms such as Winnow have been shown to have exceptionally good theoretical properties. We present an algorithm combining variants of Winnow and weighted-majority voting, and apply it to a problem in the aforementioned class: context-sensitive spelling correction. This is the task of fixing spelling errors that happen to result in valid words, such as substituting "to" for "too", "casual" for "causal", etc. We evaluate our algorithm, WinSpell, by comparing it against BaySpell, a statistics-based method representing the state of the art for this task. We find: (1) When run with a full (unpruned) set of features, WinSpell achieves accuracies significantly higher than BaySpell was able to achieve in either the pruned or unpruned condition; (2) When compared with other systems in the literature, WinSpell exhibits the highest performance; (3) The primary reason that WinSpell outperforms BaySpell is that WinSpell learns a better linear separator; (4) When run on a test set drawn from a different corpus than the training set was drawn from, WinSpell is better able than BaySpell to adapt, using a strategy we will present that combines supervised learning on the training set with unsupervised learning on the (noisy) test set.
A Bayesian hybrid method for context-sensitive spelling correction  [PDF]
Andrew R. Golding
Computer Science , 1996,
Abstract: Two classes of methods have been shown to be useful for resolving lexical ambiguity. The first relies on the presence of particular words within some distance of the ambiguous target word; the second uses the pattern of words and part-of-speech tags around the target word. These methods have complementary coverage: the former captures the lexical ``atmosphere'' (discourse topic, tense, etc.), while the latter captures local syntax. Yarowsky has exploited this complementarity by combining the two methods using decision lists. The idea is to pool the evidence provided by the component methods, and to then solve a target problem by applying the single strongest piece of evidence, whatever type it happens to be. This paper takes Yarowsky's work as a starting point, applying decision lists to the problem of context-sensitive spelling correction. Decision lists are found, by and large, to outperform either component method. However, it is found that further improvements can be obtained by taking into account not just the single strongest piece of evidence, but ALL the available evidence. A new hybrid method, based on Bayesian classifiers, is presented for doing this, and its performance improvements are demonstrated.
A Discriminative Reranking Approach to Spelling Correction
一种基于判别式重排序的拼写校正方法

ZHANG Yang,HE Pi-Lian,XIANG Wei,LI Mu,
张扬
,何丕廉,向伟,李沐

软件学报 , 2008,
Abstract: This paper proposes an approach to spelling correction. It reranks the output of an existing spelling corrector, Aspell. A discriminative model (Ranking SVM) is employed to improve upon the initial ranking, using additional features as evidence. These features are derived from state-of-the-art techniques in spelling correction, including edit distance, letter-based n-gram, phonetic similarity and noisy channel model. This paper also presents a method to automatically extract training samples from the query log chain. The system outperforms the baseline Aspell greatly, as well as the previous models and several off-the-shelf systems (e.g. spelling corrector in Microsoft Word 2003). The experimental results based on query chain pairs are comparable to that based on manually-annotated pairs, with 32.2%/32.6% reduction in error rate, respectively.
Spelling Correction in Agglutinative Languages  [PDF]
Kemal Oflazer
Computer Science , 1994,
Abstract: This paper presents an approach to spelling correction in agglutinative languages that is based on two-level morphology and a dynamic programming based search algorithm. Spelling correction in agglutinative languages is significantly different than in languages like English. The concept of a word in such languages is much wider that the entries found in a dictionary, owing to {}~productive word formation by derivational and inflectional affixations. After an overview of certain issues and relevant mathematical preliminaries, we formally present the problem and our solution. We then present results from our experiments with spelling correction in Turkish, a Ural--Altaic agglutinative language. Our results indicate that we can find the intended correct word in 95\% of the cases and offer it as the first candidate in 74\% of the cases, when the edit distance is 1.
Combining Trigram-based and Feature-based Methods for Context-Sensitive Spelling Correction  [PDF]
Andrew R. Golding,Yves Schabes
Computer Science , 1996,
Abstract: This paper addresses the problem of correcting spelling errors that result in valid, though unintended words (such as ``peace'' and ``piece'', or ``quiet'' and ``quite'') and also the problem of correcting particular word usage errors (such as ``amount'' and ``number'', or ``among'' and ``between''). Such corrections require contextual information and are not handled by conventional spelling programs such as Unix `spell'. First, we introduce a method called Trigrams that uses part-of-speech trigrams to encode the context. This method uses a small number of parameters compared to previous methods based on word trigrams. However, it is effectively unable to distinguish among words that have the same part of speech. For this case, an alternative feature-based method called Bayes performs better; but Bayes is less effective than Trigrams when the distinction among words depends on syntactic constraints. A hybrid method called Tribayes is then introduced that combines the best of the previous two methods. The improvement in performance of Tribayes over its components is verified experimentally. Tribayes is also compared with the grammar checker in Microsoft Word, and is found to have substantially higher performance.
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