Integration Of Visual Inter-word Constraints And Linguistic Knowledge In Degraded Text Recognition

Integration Of Visual Inter-word Constraints And Linguistic Knowledge In Degraded Text Recognition Tao Hong Center of Excellence for Document Analysis and Recognition Department of Computer Science State University of New York at Buffalo, Buffalo, NY 14260 t aohong@cs, buffalo, edu Abstract 1 2 3 4 Degraded text recognition is a difficult task. Given a Please fin in tire noisy text image, a word recognizer can be applied to 0.90 0.33 0.30 0.80 Fleece fill In toe generate several candidates for each word image. High- o. o5 o. 30 o. 28 o. io level knowledge sources can then be used to select a Pierce flu io lire decision from the candidate set for each word image. 0.02 0.21 0.25 0.05 In this paper, we propose that visual inter-word con- Fierce flit ill the straints can be used to facilitate candidate selection. 0.02 o. 10 o. 13 0.03 Visual inter-word constraints provide a way to link word Pieces till Io Ike images inside the text page, and to interpret them sys- 0.01 0.o6 0.04 0.02 tematically. Introduction The objective of visual text recognition is to transform an arbitrary image of text into its symbolic equivalent correctly. Recent technical advances in the area of doc- ument recognition have made automatic text recogni- tion a viable alternative to manual key entry. Given a high quality text page, a commercial document recog- nition system can recognize the words on the page at a high correct rate. However, given a degraded text page, such as a multiple-generation photocopy or fac- simile, performance usually drops abruptly([1]). Given a degraded text image, word images can be ex- tracted after layout analysis. A word image from a de- graded text page may have touching characters, broken characters, distorted or blurred characters, which may make the word image difficult to recognize accurately. After character recognition and correction based on dic- tionary look-up, a word recognizer will provide one or more word candidates for each word image. Figure 1 lists the word candidate sets for the sentence, "Please fill in the application form." Each word candidate has a confidence score, but the score may not be reliable because of noise in the image. The correct word candi- date is usually in the candidate set, but may not be the candidate with the highest confidence score. Instead of simply picking up the word candidate with the high- est recognition score, which may make the correct rate quite low, we need to find a method which can select a candidate for each word image so that the correct rate can be as high as possible. Contextual information and high-level knowledge can be used to select a decision word for each word image 5 6 7 application farm ! 0.90 0.35 applicators form 0.05 0.30 acquisition forth 0.03 0.20 duplication foam 0.01 0.11 implication force 0.01 0.04 Figure 1: Candidate Sets for the Sentence: in the application form/" "Please fill in its context. Currently, there are two approaches, the statistical approach and the structural …

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