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 86.
6. lappuse
... corresponding ductus (the order of the sequence of movements) as well as the study of the art of writing during the ... correspond to the parts drawn at the highest speed. They are also the most stable parts of the signatures and have a ...
... corresponding ductus (the order of the sequence of movements) as well as the study of the art of writing during the ... correspond to the parts drawn at the highest speed. They are also the most stable parts of the signatures and have a ...
7. lappuse
... corresponding to a catastrophe which is an abrupt change in one of the properties of this shape. Handwriting is a 2-D plane curve for which the main geometrical features are: continuity, differentiability and curvature. Therefore, we ...
... corresponding to a catastrophe which is an abrupt change in one of the properties of this shape. Handwriting is a 2-D plane curve for which the main geometrical features are: continuity, differentiability and curvature. Therefore, we ...
9. lappuse
... corresponds in some way to the trajectory of someone following a magnified version of the handwriting drawing with his finger. This characterizes some dynamic characteristics of the handwriting signal. In on-line handwriting reading ...
... corresponds in some way to the trajectory of someone following a magnified version of the handwriting drawing with his finger. This characterizes some dynamic characteristics of the handwriting signal. In on-line handwriting reading ...
22. lappuse
... corresponds to the causality of a trajectory associated with 7t. The reasons for these constraints will become clear when the learning and recognition algorithms are explained. 3.2 Recognition Learning and recognition are closely ...
... corresponds to the causality of a trajectory associated with 7t. The reasons for these constraints will become clear when the learning and recognition algorithms are explained. 3.2 Recognition Learning and recognition are closely ...
26. lappuse
... corresponding state sequence {Qe(t)} obtained by the scheme described above, one wants to see how well 711 fits to {}_{}}#1. One way of doing this is to compute — log P({O1(t)}=1, Q(T.) = qx (H1) | ?ti) where Q(T.) = q.v.(?(1), i.e., QN ...
... corresponding state sequence {Qe(t)} obtained by the scheme described above, one wants to see how well 711 fits to {}_{}}#1. One way of doing this is to compute — log P({O1(t)}=1, Q(T.) = qx (H1) | ?ti) where Q(T.) = q.v.(?(1), i.e., QN ...
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