Rieseman, E.M. and A.R. Hanson. 1974. A contextual postprocessing system for error correction using binary n-grams. In IEEE Transactions on Computers, 23(5):480-493.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....be used on a much broader range of special words. 2 Mainly to keep memory requirements down. In the meantime we also have a preliminary system that uses character n grams of arbitrary length, but we cannot give meaningful results for that yet. For a general treatmentoncharacter n grams see [6]. 82 i l 4 i l 4 N NOM SG SUBJ was be SV SVC N SVC A V PAST SG1,3 VFIN FMAINV the the Def DET CENTRAL ART SG PL DN only only A ABS AN cytokine cytokine N NOM SG PCOMPL S that that NonMod CLB Rel PRON SG PL SUBJ ....

Riseman, E.M. and Hanson, A.R., A contextual postprocessing system for error correction using binary n-grams, IEEE Transactions on Computers, C-23(5):480--493, 1974.

....be used on a much broader range of special words. 2 Mainly to keep memory requirements down. In the meantime we also have a preliminary system that uses character n grams of arbitrary length, but we cannot give meaningful results for that yet. For a general treatment on character n grams see [6]. 82 i l 4 i l 4 N NOM SG SUBJ was be SV SVC N SVC A V PAST SG1,3 VFIN FMAINV the the Def DET CENTRAL ART SG PL DN only only A ABS AN cytokine cytokine N NOM SG PCOMPL S that that NonMod CLB Rel PRON SG PL SUBJ ....

Riseman, E.M. and Hanson, A.R., A contextual postprocessing system for error correction using binary n-grams, IEEE Transactions on Computers, C-23(5):480--493, 1974.

....an excellent survey of approximate string search methods is presented. Other more specific techniques exist, like the methods that try to benefit from the high performance of exact search algorithms, by generating a number of neighboring strings to the one sought for, and searching all of them [12]. Unfortunately, that neighborhood can grow exponentially with the number of symbols of the string, and thus heuristic and probabilistic constraints have to be imposed. In other cases, the lexicon is subdivided according to different criteria, like the length of the words, the first symbols, ....

E. Riseman and A. Hanson. A contextual postprocessing system for error correction using binary n-grams. IEEE Trans. Computer, 23:480--493, 1974.

....application, such a high recognition rate may be still insufficient. In order to further improve recognition accuracy, contextual postprocessing is often very useful. Different contextual postprocessing methods have been proposed in the literature. They are based, for example, on n gram statistics [3,4], or dictionary search [5,6] A recent survey on contextual processing has been given in [7] For earlier overviews see [8,9] In the present paper we propose the application of finite state automata and errorcorrecting parsing to solve a particular postprocessing problem occurring in the context ....

Riseman, E.M. and Hanson, A.R.: A contextual postprocessing system for error correction using binary n-grams, IEEE Trans. on Computers, Vol. C-23, May 1974, 480-493

....reasons. First, the purpose of the implementation was primarily to develop the spatio temporal connectionist components and benchmark their base discriminatory capabilities. Second, a substantive amount of work has been done on incorporating domain knowledge into word recognition (Doster, 1977; Riseman and Hanson, 1974; Shingal and Toussaint, 1979) Typically, a ZIP code consists of either 5 or 9 digits. This knowledge can be used by the PC to maintain a running estimate of how many digits remain to be seen, and use this estimate to guide the segmentation and recognition process. The frequencies of two ....

.... a separate stage, however, since algorithms taking into account the underlying distributions can easily be employed not only to produce more confident classifications based on available domain knowledge, but also to produce ranked hypotheses concerning missing or extra digits (Doster, 1977; Riseman and Hanson, 1974; 31 Shingal and Toussaint, 1979) 9 5.4 System Results Results on the problems of overlapping digit pair recognition and USPS ZIP code recognition are now presented. The PC utilized no domain knowledge regarding individual character form, frequency, or contextual dependencies. In addition, no ....

Riseman, E. and Hanson, A. (1974). A contextual postprocessing system for error correction using binary n-grams. IEEE Transactions on Computers, 23:480--493.

....this intuitive approach may be suitable for applications which require only a small lexicon, it becomes increasingly expensive as the size of the dictionary grows. Therefore, other methods have been developed to abstract or compact the information contained in the dictionary. Riseman and Hanson [27] use inter character relationships within words to Input W O R D Isolated Character Recognition Preprocessing WORO Contextual Postprocessing Dictionary WORD ICR Output Corrected Output (with mistakes ) Figure 2.3: Character based Word Recognition with Contextual Postprocessing 14 Input W O ....

Edward M. Riseman and Allen R. Hanson. A contextual postprocessing system for error correction using binary n-grams. IEEE Transactions on Computers, C-23:480--493, May 1974.

....method uses C 2 n positionspecific digrams for words that are of a particular length n. The method is intended to eliminate invalid words when a character recognizer is unable to discriminate among candidate characters in a small substitution set. The method is generalized to binary n grams in [108]. N gram methods are good for efficient elimination of illegal strings. However, simple n grams are not too useful when the set of valid strings is large. Position specific n grams cannot handle missegmentations which are common. Moreover, priorities among candidate character classes in a ....

E.M. Riseman, R.W. Ehrich, A Contextual Postprocessing System for Error Correction Using Binary N-grams, IEEE Transactions on Computers, C-23, 5, May 1974, 480-493.

....[157] This is because the probability of a sequence of words is not obtained by multiplying the probabilities of each word. Besides language modeling for speech recognition, N gram based probabilistic models are also used in the area of optical and handwritten character recognition ( 54] [123] and [138] However, according to a recent survey on optical character recognition ( 58, p.11] the N gram model does not come under the name of language model, but is referred to as contextual processing and is one part of the postprocessing in optical character recognition. Nevertheless, the ....

E. Riseman and A. Hanson. A contextual postprocessing system for error correction using binary N-grams. IEEE Transactions on Computers, 23:480--493, 1974. BIBLIOGRAPHY 137

....tries that optimize the r error digital search problem (where r digits of the key may be erroneous) but concludes that the problem of finding the optimal tree is NP complete. Other spelling correction systems have used hashing (Peterson, 1980) and digrams, trigrams, or in general ngrams (Riseman Hanson, 1974). However, it would be difficult to apply these indexing strategies to the diversity of operations that cross indexing supports. Smith and Steen (1981) proposed using a bitmap approach similar to cross indexing, for their crossword compiler. They use separate bitmaps for each word length, whereas ....

Riseman, E.M and Hanson, A.R. (1974) A contextual postprocessing system for error correction using binary ngrams.

....very useful. A recent survey of contextual postprocessing methods has been given in [5] For an earlier collection of papers addressing the same problem domain see [6] There are different categories of contextual postprocessing methods. One class of methods is based on n gram statistics [7,8]. Such methods rely on transition probabilities between consecutive letters of a word. As an advantage of n gram based methods, these transition probabilities can be determined off line, for example, from a dictionary of legal words. Consequently, the run time needed online for error detection or ....

Riseman, E.M. and Hanson, A.R.: A Contextual Postprocessing System for Error Correction Using Binary n-Grams, IEEE Trans. on Computers, Vol. C23, May 1974, 480-493

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Rieseman, E.M. and A.R. Hanson. 1974. A contextual postprocessing system for error correction using binary n-grams. In IEEE Transactions on Computers, 23(5):480-493.

No context found.

E. M. Riseman and A. R. Hanson, A Contextual Post Processing System for Error Correction using binary n-grams, IEEE Transactions on Computer, vol. C-23, pp 480-493, 1974.

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E. M. Riseman, A. R. Hanson, "A contextual Postprocessing System for Error Correction Using Binary n-Grams", Trans. on COMPUTER IEEE, Vol. C-23, No.5, MAY, pp.480-493, 1974.

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Riseman EM, Ehrich RW (1974), A Contextual Postprocessing System for Error Correction Using Binary N-grams. IEEE Transactions on Computers, C-23(5):480-493

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