Chang Jason J. S.

Chen Shun-Der

English

Chang, Jason J. S.

49136

Chen, Shun-Der

49137

Waseda University Repository

The Postprocessing of Optical Character Recognition based onStatistical Noisy channel and language modelJason J. S. Chang and Shun-Der ChenDepartment of computer Science , National Tsing Hua Universitye-mail jschang@cs.nthu.edu.twdr798306®cs.nthu.edu.twAbstractThe techniques of image processing have been used in optical character recognition (OCR)for a long time. The recognition method evolved from early "pattern recognition" to "featureextraction" recently. The recognition rate is raised from 70% to 90%. But the character bycharacter recognition technique has its limitation. Using language models to assist the OCRsystem in improving recognition rate is the topic of many recent researches.Recently, the related research on Chinese nature language processing has improved rapidly.These improvement include the Chinese word segmentation, syntax analysis, semanticanalysis, collocation analysis, statistical language models.In this paper, we will propose a new techniques for Chinese OCR postprocessing andpostediting. We combine noisy channel model and the technique of natural languageprocessing to implement an OCR postprocessing system. From the result of experiments, wefound noisy channel model very effective for postprocessing. Under the approach, it ispossible to recover the correct character, even when it is not in the candidate list producedby the OCR system.1. IntroductionRecently, the research of human-machine interface is becoming more and more important.To reduce the workload of user entering text and data into a computer system, there aremany human-machine interfaces developed, such as optical character recognition (OCR),speech recognition, etc. However, some errors are usually introduced in the recognitionprocess. Therefore, means for finding and correcting such errors in indispensable.Research in character recognition for Chinese faces more difficulties than for otherlanguages. Firstly, Chinese has a huge character set. Secondly, Chinese characters have morecomplex structure than alphabetic characters and there are a large number of similarcharacter groups. Therefore, we need some kind of contextual information to detect andcorrect errors. Language models can be used to provide such contextual information toenhance the correction rate and speed of a man-machine interface system. For example,Tsuyoshi [14] use character frequency and the morphological analysis to improve thehandwritten Japanese OCR system.2000 -15001000c.)500 -0 0 0, en	 VI OSei N cn co) coterror count /100'errorcorrecte 001/1Recently the techniques of Chinese OCR have advanced greatly, the recognition rate hasreached 95% and 90% for printed and handwritten text respectively. In view of thelimitation of the techniques of OCR, we hope to use natural language models to detect the 5to 10% errors and suggested their possible correction in a postediting environment. With aneffective postprocessing technique and user-friendly posteditor, the usability of OCRtechnology can be greatly enhanced.2. The framework of the System  Image Recognition Unit Post Processing UnitChinese PrintText data>/ Chinese OCRRecognitionSystemImage	 	 StatisticalmodelDetectionNaturalLanguageText Filc.< Correction Model Noisy ChannelModelIn this paper, we have adopted a multi-stage postprocessing approach. In the detection stage,the statistical model is used to re-evaluate the confidence of the error counts provided by theimage model. The purpose of this stage is to identify the places where the first candidate iswrong. In correction stage, we combine the noisy channel and language model to suggestpossible corrections.3. The Error Detection ModelTo evaluate the confidence of the error counts, we analyse the output produced by therecognition system (UMAX Standard Chinese OCR System). First, we scan the mostfrequent 5401 Chinese characters. And we analyzed the relationship between the number ofthe correct candidates with a certain error count (see Figure 1). Using the distribution of therelation, we can come up with a threshold for detecting point when the first candidate is inerror. To further improve the precision of detection, a dictionary is used to reconfirm thesedetection points.Figure 1. number of correct candidate vs. error count1284. The Error Correction Model4.1 Language ModelIn the first experiment of the correction stage, we used the word segmentation model [18]locally to correct the detected points. This was very effective in correcting errors found inlong words consisting of 2 or more characters. As for single-character word, the characterbigram model was used. We trained the character bigram information from a 300,000Chinese character balance corpus. From the result of experiment, the correction stageeliminated 2 thirds of the error produced by OCR.4.2 Noisy Channel ModelOur previous work[20] shows that the upmost difficultly in postprocessing is found in thecase where the correct character is missing from the candidate list. Therefore, we exploitnoisy channel model to introduce some characters into the candidate list to resolve theproblem. In our experiment, that can be done successfully for 40% the case.Noisy Channelcorrect characternot in candiadte correct charactmay be rebackin candidate 5. ConclusionThis paper proposes a new approach to the OCR postprocessing. It combines theinformation provided by the image processing unit and contextual information to detect therecognition errors. In the past, the OCR system can not solve the problem of the missingcorrect character in the candidate list. In out experiment, we find it that the noisy channelmodel is very effective in solving this problem.In the future, we will enhance the noisy channel model and introduce the long distancecontextual information to improve our system. It seems that this model is applicable to otherman-machine interface applications. We are currently studying the possibility of apply themodel to speech recognition.Table 1: The experiment result of error detectionTitle WiF-14--i.t iiiIth**44-0total errors find in first candidate 45 total errors find in first candidate 73alarms truepositivePrecision Recall alarms truepositivePrecision RecallUMAX 90 19 21.1% 42.2 122 31 25.4% 42.5%Threshhold 221 45 20.4%  100%. 272 72 26.5% 98.6%Dictionary 94 43 46% 95.6% 163 71 _	 43.6%	 _ 97.3%129Table 2 : The experiment result of error correctiontotal errors find in first candidate 45alarms detection(truepositive)alarmprecision(uncorrecterror)correction(falsenegative)correction(truepositive)correctrateUMAX 90 19 54% 35 10 22%Word Segment 68 19 79% 24 21 47%Noisy Channel 68 14 74% 19 26 58%Character Bigram_	 68 9 69% 13 32 71% ,0.***440 Atotal errors find in first candidate 73alarms detection(truepositive)alarmprecision(uncorrecterrors)correction(falsenegative)correction(truepositive)correctrateUMMAX 122 31 57% 54 19 26%Word Segment 109 36 86% 42 31 43% ,,Noisy Channel 109 31 86% 36 37 51%Character	 gram 109 22 85% 26 47 64%* alarm precision (uncorrect errors) = detection (true positive) / correction (false negative)Reference[1] Andrew R. Golding, A Bayesian hybrid method for context-sensitive spelling correction. InProceeding of the third workshop on Very Large Corpora, 30 June, 1995.[2] Chao-Huang Chang, Word Class Discovery for Postprocessing Chinese HandwritingRecognition. In Proceeding of COLING-94, Page 1221-1225, 1994.[3] D. G. Elliman, A Review of Segmentation and Contextual Analysis Techniques for TextRecognition. Pattern Recognition, Vol 23, No. 3/4, pp. 337-346,1990.[4] Frank Smadja, Retrieving Collocations from Text: Xtract, 1993 Association for ComputationLinguistics.[5] H. Takahashi, etc., A Spelling Correction Method and its Application to an OCR System.Pattern Recognition, Vol 23, No. 3/4, pp. 363-377, 1990.[6] K. T. Lua and K. W. Gan, Recognizing Chinese Characters Through Interactive Activation andCompetition. Pattern Recognition, Vol 23, No. 12, pp. 1311-1321, 1990.[7] Mark D. Kernighan, Kenneth W. Church, and William A. Gale, A Spelling Correction ProgramBased on a Noisy Channel Model. In Proceeding of COLING-90, vol 2, Page 205-210,1990.[8] Masaki YAMASHINA & Fumihiko OBASHI, Collocation Analysis in Japanese Text Input.[9] Jyn-Sheng Chang and Yuh-Juh Lin, An estimation of the entroy of Chinese - A new approach tobuilding class-based n-gram models. Rocling VII, 1994.130[10] Rose & L.J. Evett, Text Recognition using Collocations and Domain Codes. In Proceedings ofthe Workshop on Very Large Corpora: Academic and Industrial Perspectives, pp.65-73,Columbus, Ohlo, 1993.[11] Tao Hong, Integration of Visual Inter-word Constraints and Linguistic Knowledge in DegradedText Recognition. In Proceeding of 32nd Annual Meeting of Association for ComputationalLinguistics, pp. 328-330, Las Cruces, New Mexico, 27-30 June, 1994.[12] Tao Hong and Jonathan J. Hull, Degraded Text Recognition Using Word Collocation and VisualInter-word Constraints.[13] Tetsuo Araki et al., An Evaluation of a Method to Detect and Correct Erroneous Characters inJapanese input through an OCR using Markov Models.[14] Tsuyoshi Kitani, An OCR Post-Processing method for Handwritten Japanese Documents.[15] YASUHITO TANAKA & SHO YOSHIDA, The Acquisition of Knowledge Data for NaturalLanguages — Word-to-Word Relationships Obtained by Analyzing Data Found in the AsahiNewspaper[16] c,	 (Chao-Huang Chang) ' rt7	 t 4n-ig 4ntit )[17] o 4•14-4. SC k *ILE iVat	 Rocling ' 1992.[18] itc*,§1 Ftt, it • P*Att fric*	 it ti IPSC E p7 t Rocling IV, 1991.[19]3..*4-: IA* 0-.14-44.+kt-tilePtiCE04)41. Rocling VII, 1994.[20] k4 **VA kVA-.1e-*414,	 , 1994Output Example:[Example 1]	 [Example 2]4314-ii_h6&#4****-41VrAte'l*-1- .	401411.****00000000000000000000XX0000XX00000000 	 XX00000000000044LA ICI as& itif iz	 *	 *VT* *It*#########mitioutnniorrmmutioit 	 nit#####################################nitmow 	 n############441-4.4-E .36 it* al	 —titrito**	 iit*41-****-1$################PcNitiM73####### 	 ??############4,V4.4,1-6014-41:- iti.A6 —it S	 4t14.14***##################?dmditrd??######## 	 Ndit$##########the output of UMAX Chinese OCR system10 { 04872 t 04973 t 05178 4t 05210 a. 05243 * 05252 * 05281. 05320 k. 05329 O. 0533310 * 03713 * 04832 k 04944 It 05152 k 05169 It 05274 4 05288 t 05320 * 05425 it 0543310 4 03538 izt 04202 44- 04295 4. 04529 4 04750 47 047514 04787 4 04794 % 04826 4+ 0491710 02347 ft 03497 if 03713 If 03990 04104 41- 04175 4 04181 .91- 04207 * 04245 .0F 0430310 t 03206 41.. 04445 17. 04627 $ 046314' 04715 4 04741 it 04894 47 04930 04977 4. 0500010 * 03501 *04834 * 05165 * 05238 * 05294 05315 it 05379 * 05422 * 05428 0543610 * 03293 * 04313 * 04633 If 04738 * 04765 4 04786 04803 * 04848 * 04853 04859131132

The Postprocessing of Optical Character Recognition Based on Statistical Noisy Channel and Language Model

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The Postprocessing of Optical Character Recognition Based on Statistical Noisy Channel and Language Model

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