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 91.
6. lappuse
... letters previously drawn in stone or clay tablets and that, for this reason, they were drawn by descending movements ... letter shapes such as uncial, Caroline script, etc. • Clues from Tools The old writing instruments (calamus, quill ...
... letters previously drawn in stone or clay tablets and that, for this reason, they were drawn by descending movements ... letter shapes such as uncial, Caroline script, etc. • Clues from Tools The old writing instruments (calamus, quill ...
8. lappuse
... letter strokes (i.e. a kind of kernel for each letter). These downstrokes are partially connected together depending on the vertical position of the pen tip (up or down). From the different kinds of primitives, of basic strokes and of ...
... letter strokes (i.e. a kind of kernel for each letter). These downstrokes are partially connected together depending on the vertical position of the pen tip (up or down). From the different kinds of primitives, of basic strokes and of ...
10. lappuse
... letters or of the diacritics. This is done in order to decide if a stroke or a letter is on the left or on the right side, above or under another one or if it has an extension over or under the median zone, etc. (e.g. as for “d”, “b ...
... letters or of the diacritics. This is done in order to decide if a stroke or a letter is on the left or on the right side, above or under another one or if it has an extension over or under the median zone, etc. (e.g. as for “d”, “b ...
11. lappuse
... letters is also an evidence to distinguish the capital style from the small style of a letter. The perception of the relative position of a letter in a word is also an evidence of the fact that a character may be a capital letter if it ...
... letters is also an evidence to distinguish the capital style from the small style of a letter. The perception of the relative position of a letter in a word is also an evidence of the fact that a character may be a capital letter if it ...
29. lappuse
... letters not strictly all separated or all connected within words. To allow users to write naturally and be recognized accurately, it is therefore frequently necessary for them to go through a training process in which they provide ...
... letters not strictly all separated or all connected within words. To allow users to write naturally and be recognized accurately, it is therefore frequently necessary for them to go through a training process in which they provide ...
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