Advances In Handwriting RecognitionSeong-whan Lee World Scientific, 1999. gada 1. jūn. - 600 lappuses Advances in Handwriting Recognition contains selected key papers from the 6th International Workshop on Frontiers in Handwriting Recognition (IWFHR '98), held in Taejon, Korea from 12 to 14, August 1998. Most of the papers have been expanded or extensively revised to include helpful discussions, suggestions or comments made during the workshop. |
No grāmatas satura
1.5. rezultāts no 78.
20. lappuse
... recognition rate was 85.35% while the top five recognition rate was 97.36%. Main contributions of this paper include; (i) After a preprocessing, input strokes are discretsized in a particular manner which naturally leads to simple ...
... recognition rate was 85.35% while the top five recognition rate was 97.36%. Main contributions of this paper include; (i) After a preprocessing, input strokes are discretsized in a particular manner which naturally leads to simple ...
27. lappuse
... recognized and Fig. 5.2 gives examples of erroneously classified characters. Table 5.1 Recognition Rates data recognition rate (%) top five recognition rate (%) Kanji 91.71 97.98 Hiragana 79.08 96.88 Katakana 69.15 95.17 Overall 85.89 ...
... recognized and Fig. 5.2 gives examples of erroneously classified characters. Table 5.1 Recognition Rates data recognition rate (%) top five recognition rate (%) Kanji 91.71 97.98 Hiragana 79.08 96.88 Katakana 69.15 95.17 Overall 85.89 ...
28. lappuse
... recognition rate is 97.36%, a significant improvment recognition performance may be possible by exploiting grammatical structure associated with character sequence instead of individual characters. This is one of the challenging future ...
... recognition rate is 97.36%, a significant improvment recognition performance may be possible by exploiting grammatical structure associated with character sequence instead of individual characters. This is one of the challenging future ...
36. lappuse
... accuracy for the basic test. Results show percent change in error rate relative to training on text T1. Relative Change Relative Change Training Text in Word Error (%) in Character Error (%) BT / CT 0.8 / 7.7 2.9 / 6.6 B / C +9.4 ...
... accuracy for the basic test. Results show percent change in error rate relative to training on text T1. Relative Change Relative Change Training Text in Word Error (%) in Character Error (%) BT / CT 0.8 / 7.7 2.9 / 6.6 B / C +9.4 ...
37. lappuse
... recognition search. Instead, we used a character 4-gram language model, with no dictionary constraint, at some cost to accuracy on general text. Breaking performance down by character type in Table 2 reveals that the error patterns ...
... recognition search. Instead, we used a character 4-gram language model, with no dictionary constraint, at some cost to accuracy on general text. Breaking performance down by character type in Table 2 reveals that the error patterns ...
Saturs
17 | |
HANDWRITTEN FORM PROCESSING | 79 |
HANDWRITTEN WORD RECOGNITION | 151 |
SEGMENTATION | 223 |
ORIENTAL SCRIPT PROCESSING | 275 |
NUMERAL RECOGNITION | 357 |
EMERGING TECHNIQUES | 437 |
APPLICATIONS | 517 |
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algorithm applied approach B-spline bankcheck bigram character candidates character line character recognition character segmentation classifier combination computed connected components contour corresponding courtesy amount database described detected diacriticals dictionary digit distance error rate evaluation example experimental results experiments feature extraction feature set feature vectors Figure function fuzzy graph grapheme handwriting recognition handwritten character handwritten numerals handwritten words Hidden Markov Models horizontal Hough transform hypotheses integrated Kanji learning legal amount letters lexicon line segments matching matrix nat-ja neural network node obtained off-line on-line handwriting recognition optimal output paper parameters Pattern Recognition performance pixels points preprocessing probability problem Proc proposed method prototype quantization recognition rate recognition results recognition system recognizer samples sequence shown speech recognition step string stroke structure Suen Table technique template threshold touching type vector quantizer wavelet word recognition writing