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 68.
12. lappuse
... recognizer makes errors depending on whether the comparison is mostly made on the pertinent information or on the “noise”, i.e. the variable part of the handwriting. What is suggested in this paper is to use only an optimal number of ...
... recognizer makes errors depending on whether the comparison is mostly made on the pertinent information or on the “noise”, i.e. the variable part of the handwriting. What is suggested in this paper is to use only an optimal number of ...
15. lappuse
... Recognizer Combination Topologies”, in Handwriting and Drawing Research: Basic and Applied Issues, IOS Press, pp. 329-342, 1996. J. Kittler et al., “On Combining Classifiers”, IEEE Trans on PAMI. PAMI20,3, pp.226-239, 1998. C.J. Wells ...
... Recognizer Combination Topologies”, in Handwriting and Drawing Research: Basic and Applied Issues, IOS Press, pp. 329-342, 1996. J. Kittler et al., “On Combining Classifiers”, IEEE Trans on PAMI. PAMI20,3, pp.226-239, 1998. C.J. Wells ...
29. lappuse
... recognized accurately, it is therefore frequently necessary for them to go through a training process in which they provide samples of their own writing so that the recognizer can adapt to their styles? Such writer-dependent recognition ...
... recognized accurately, it is therefore frequently necessary for them to go through a training process in which they provide samples of their own writing so that the recognizer can adapt to their styles? Such writer-dependent recognition ...
30. lappuse
... recognizer in the IBM. Ink. Manager*. software. Data. are. collected. as. a. stream. of. (x,. y). points. indexed. in time. At each point, we compute features based on distances and angles among points. Windows of adjacent points are ...
... recognizer in the IBM. Ink. Manager*. software. Data. are. collected. as. a. stream. of. (x,. y). points. indexed. in time. At each point, we compute features based on distances and angles among points. Windows of adjacent points are ...
35. lappuse
... recognizer). In the case of read text, they were asked to write capital letters whenever the text showed capitals. For audio stimuli, they were asked to listen to one line and then write it before listening to the next line, rewinding ...
... recognizer). In the case of read text, they were asked to write capital letters whenever the text showed capitals. For audio stimuli, they were asked to listen to one line and then write it before listening to the next line, rewinding ...
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