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 60.
x. lappuse
... Digits............................................................... 359 X. Wang, W. Govindaraju and S. Srihari ... Digit Recognition...................................................... 426 Z. Lu, Z. Chi, W-C. Siu and P-F Shi PART ...
... Digits............................................................... 359 X. Wang, W. Govindaraju and S. Srihari ... Digit Recognition...................................................... 426 Z. Lu, Z. Chi, W-C. Siu and P-F Shi PART ...
30. lappuse
... digits, and 31 other symbols, approximately those on a typical American-English-language computer keyboard. The writer-dependent version of our system uses a pool of 200 Gaussian distributions, with one model per character, the 93 ...
... digits, and 31 other symbols, approximately those on a typical American-English-language computer keyboard. The writer-dependent version of our system uses a pool of 200 Gaussian distributions, with one model per character, the 93 ...
31. lappuse
... digits and symbols such as “%”. We determined that we could explore the three text lengths, the two coverage criteria, and the three points along the text priority scale, using just seven texts of 250 words, as follows: • Texts T1, T2 ...
... digits and symbols such as “%”. We determined that we could explore the three text lengths, the two coverage criteria, and the three points along the text priority scale, using just seven texts of 250 words, as follows: • Texts T1, T2 ...
33. lappuse
... digit in text BT and 10 each in B, and 3 occurrences of each symbol, each capital letter in a mixed-case environment and each capital letter in an all-capitals environment in text BT, 6 each in B. Text CT includes 602 distinct character ...
... digit in text BT and 10 each in B, and 3 occurrences of each symbol, each capital letter in a mixed-case environment and each capital letter in an all-capitals environment in text BT, 6 each in B. Text CT includes 602 distinct character ...
34. lappuse
... digits, and other symbols – be sufficiently represented to enable computing fairly sensitive accuracy statistics, so ... digit. To achieve even these coverage criteria this with reasonably natural text, we required a 2500-character test ...
... digits, and other symbols – be sufficiently represented to enable computing fairly sensitive accuracy statistics, so ... digit. To achieve even these coverage criteria this with reasonably natural text, we required a 2500-character test ...
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