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 83.
xii. lappuse
... L. Claesen An Efficient Function-based On-line Signature Recognition System................... 559 K. K. Lau, P. C. Yuen and Y. Y. Tang Signature Verification Using Distribution of Angular Direction of Pen-point Movement ...
... L. Claesen An Efficient Function-based On-line Signature Recognition System................... 559 K. K. Lau, P. C. Yuen and Y. Y. Tang Signature Verification Using Distribution of Angular Direction of Pen-point Movement ...
21. lappuse
... function, and HMM is no exception. The general framework of HMM must be carefully tuned to the on-line handwritten character. 3 Fast HMM for On-line Handwritten Character Recognition V 3 Fig. 3.4 Left-to-right model. 21 3 Fast HMM for On ...
... function, and HMM is no exception. The general framework of HMM must be carefully tuned to the on-line handwritten character. 3 Fast HMM for On-line Handwritten Character Recognition V 3 Fig. 3.4 Left-to-right model. 21 3 Fast HMM for On ...
29. lappuse
... function of three dirnensions of training text: length, choice of character-coverage criterion, and relative priority of keeping the text interesting vs. optimizing to the chosen character-coverage criterion. Our results show various ...
... function of three dirnensions of training text: length, choice of character-coverage criterion, and relative priority of keeping the text interesting vs. optimizing to the chosen character-coverage criterion. Our results show various ...
35. lappuse
... function which erased their last stroke (pen-down to pen-up), allowing them to correct writing errors (e. g. misspelling), while preserving the order of writing the words. They were also provided a self-paced practice session to ...
... function which erased their last stroke (pen-down to pen-up), allowing them to correct writing errors (e. g. misspelling), while preserving the order of writing the words. They were also provided a self-paced practice session to ...
47. lappuse
... function to guide the search, may help in reducing the search time. It, however, is beyond the scope of this paper and will be investigated in our future research. 9 Conclusion Our experiment shows that, by making use of structural ...
... function to guide the search, may help in reducing the search time. It, however, is beyond the scope of this paper and will be investigated in our future research. 9 Conclusion Our experiment shows that, by making use of structural ...
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