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 36.
19. lappuse
... preprocessing input stroke are discretized so that a discrete HMM is used. This particular discretization naturally leads to a simple procedure for assigning initial state and state transition probabilities. In the training phase ...
... preprocessing input stroke are discretized so that a discrete HMM is used. This particular discretization naturally leads to a simple procedure for assigning initial state and state transition probabilities. In the training phase ...
20. lappuse
... preprocessing, input strokes are discretsized in a particular manner which naturally leads to simple procedures for assigning initial state and state transition probabilities; (ii) In the training phase, complete marginalization with ...
... preprocessing, input strokes are discretsized in a particular manner which naturally leads to simple procedures for assigning initial state and state transition probabilities; (ii) In the training phase, complete marginalization with ...
42. lappuse
... preprocessing stage or some later stages. Due to the paper limit, we will not explain here details of the algorithms used to tackle these problems. The details can be found in our other paper”. 5 Elastic Structural Matching After ...
... preprocessing stage or some later stages. Due to the paper limit, we will not explain here details of the algorithms used to tackle these problems. The details can be found in our other paper”. 5 Elastic Structural Matching After ...
50. lappuse
... preprocessing module. Each of these vectors typically represents geometrical properties of a short contiguous ink fragment (see Figure 1b). Removing delayed vectors from this sequence" or trying to reinsert them at a better position ...
... preprocessing module. Each of these vectors typically represents geometrical properties of a short contiguous ink fragment (see Figure 1b). Removing delayed vectors from this sequence" or trying to reinsert them at a better position ...
55. lappuse
Esat sasniedzis šīs grāmatas aplūkošanas reižu limitu.
Esat sasniedzis šīs grāmatas aplūkošanas reižu limitu.
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