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 64.
ii. lappuse
... Learning and Perception (Eds. G. Tasini, F. Esposito, V. Roberto and P. Zingarett) Spatial Computing: Issues in Vision, Multimedia and Visualization Technologies (Eds. T. Caeli, Peng Lam and H. Bunke) Studies in Pattern Recognition ...
... Learning and Perception (Eds. G. Tasini, F. Esposito, V. Roberto and P. Zingarett) Spatial Computing: Issues in Vision, Multimedia and Visualization Technologies (Eds. T. Caeli, Peng Lam and H. Bunke) Studies in Pattern Recognition ...
vii. lappuse
... Learning............ 19 H. Yasuda, K. Takahashi and T. Matsumoto Optimization of Training Texts for Writer-dependent Handwriting Recognition...................................................... 29 J. F. Pitrelli, J. Subrahmonia, M. P. ...
... Learning............ 19 H. Yasuda, K. Takahashi and T. Matsumoto Optimization of Training Texts for Writer-dependent Handwriting Recognition...................................................... 29 J. F. Pitrelli, J. Subrahmonia, M. P. ...
viii. lappuse
... Learning Algorithm Based on Hough Transform for Text Lines Extraction in Handwritten Documents........................................... 141 Y. Pu and Z. Shi PART 4: HANDWRITTEN WORD RECOGNITION Handwritten Word Recognition The ...
... Learning Algorithm Based on Hough Transform for Text Lines Extraction in Handwritten Documents........................................... 141 Y. Pu and Z. Shi PART 4: HANDWRITTEN WORD RECOGNITION Handwritten Word Recognition The ...
x. lappuse
... Learning...... 378 J. Franke Distinctiveness and Similarities of Handwritten Numerals................................... 387 Z. C. Li and C. Y. Suen Recognition of Handwritten Numerals Using Multiple Features and Multiple Classifiers ...
... Learning...... 378 J. Franke Distinctiveness and Similarities of Handwritten Numerals................................... 387 Z. C. Li and C. Y. Suen Recognition of Handwritten Numerals Using Multiple Features and Multiple Classifiers ...
6. lappuse
... learning such movements e.g. those which are still found in the calligraphic art. Clues from Biomechanics Biomechanics shows that there are very difficult or impossible combined movements of the wrist and of the hand. This explains ...
... learning such movements e.g. those which are still found in the calligraphic art. Clues from Biomechanics Biomechanics shows that there are very difficult or impossible combined movements of the wrist and of the hand. This explains ...
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