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 84.
ix. lappuse
... : SEGMENTATION Global Methods for Stroke Segmentation ............................................................. 225 Y. Nakajima, S. Mori, S. Takegami and S. Sato Robust Deformable Matching for Character Extraction ...............
... : SEGMENTATION Global Methods for Stroke Segmentation ............................................................. 225 Y. Nakajima, S. Mori, S. Takegami and S. Sato Robust Deformable Matching for Character Extraction ...............
8. lappuse
... stroke into two others. – Basic Allograph Models Allographs are different drawings of a same character according to ... strokes (i.e. a kind of kernel for each letter). These downstrokes are partially connected together depending on the ...
... stroke into two others. – Basic Allograph Models Allographs are different drawings of a same character according to ... strokes (i.e. a kind of kernel for each letter). These downstrokes are partially connected together depending on the ...
9. lappuse
... stroke drawn very quickly but without any information in it, or a handwriting with a lot of missing information (e.g. a physician's prescription). • Polysemy At each level (stroke or graphemic level, letter or lexical level, word or ...
... stroke drawn very quickly but without any information in it, or a handwriting with a lot of missing information (e.g. a physician's prescription). • Polysemy At each level (stroke or graphemic level, letter or lexical level, word or ...
10. lappuse
... strokes, of the 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 ...
... strokes, of the 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 ...
11. lappuse
... strokes and placed in the same relative positions). For example, “a” and “d” differ only by the size of the elongate stroke on their right side, “e” and “l " are only distinguished by the length of their loop. The perception of the ...
... strokes and placed in the same relative positions). For example, “a” and “d” differ only by the size of the elongate stroke on their right side, “e” and “l " are only distinguished by the length of their loop. The perception of the ...
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