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Design of a linguistic postprocessor using variable memory length Markov models
Proceedings of 3rd International Conference on Document Analysis and Recognition
Year: 1995 | Conference Paper |
We describe a linguistic postprocessor for character recognizers. The central module of our system is a trainable variable memory length Markov model (VLMM) that predicts the next character given a variable length window of past characters. The overall system is composed of several finite state automata, including the main VLMM and a proper noun VLMM. The best model reported in the literature (Bro...Show More
UNIPEN project of on-line data exchange and recognizer benchmarks
We report the status of the UNIPEN project of data exchange and recognizer benchmarks started two years ago at the initiative of the International Association of Pattern Recognition (Technical Committee 11). The purpose of the project is to propose and implement solutions to the growing need of handwriting samples for online handwriting recognizers used by pen-based computers. Researchers from sev...Show More
Comparison of classifier methods: a case study in handwritten digit recognition
L. Bottou;C. Cortes;J.S. Denker;H. Drucker;I. Guyon;L.D. Jackel;Y. LeCun;U.A. Muller;E. Sackinger;P. Simard;V. Vapnik
This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also training time, recognition time, and memory requirements. When available, we report measurements of the fraction of patterns that must be rejected so that the remaining patterns have misclassification rates less than a given threshold.Show More
On-line cursive script recognition using time delay neural networks and hidden Markov models
Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 1994 | Conference Paper |
Presents a writer independent system for on-line handwriting recognition which can handle both cursive script and hand-print. The pen trajectory is recorded by a touch sensitive pad, such as those used by note-pad computers. The input to the system contains the pen trajectory information, encoded as a time-ordered sequence of feature vectors. Features include X and Y coordinates, pen-lifts, speed,...Show More
Writer-adaptation for on-line handwritten character recognition
Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93)
Year: 1993 | Conference Paper |
The authors have designed a writer-adaptable character recognition system for online characters entered on a touch terminal. It is based on a Time Delay Neural Network (TDNN) that is pre-trained on examples from many writers to recognize digits and uppercase letters. The TDNN without its last layer serves as a preprocessor for an optimal hyperplane classifier that can be easily retrained to peculi...Show More
Computer aided cleaning of large databases for character recognition
Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems
Year: 1992 | Conference Paper |
Cited by: Papers (16)
A method for computer-aided cleaning of undesirable patterns in large training databases has been developed. The method uses the trainable classifier itself, to point out patterns that are suspicious, and should be checked by the human supervisor. While suspicious patterns that are meaningless or mislabeled are considered garbage, and removed from the database, the remaining patterns, like ambiguo...Show More
Capacity control in linear classifiers for pattern recognition
Achieving good performance in statistical pattern recognition requires matching the capacity of the classifier to the amount of training data. If the classifier has too many adjustable parameters (large capacity), it is likely to learn the training data without difficulty, but will probably not generalize properly to patterns that do not belong to the training set. Conversely, if the capacity of t...Show More
Hardware requirements for neural-net optical character recognition
L.D. Jackel;B. Boser;J.S. Denker;H.P. Graf;Y. Le Cun;I. Guyon;D. Henderson;R.E. Howard;W. Hubbard;S.A. Solla
1990 IJCNN International Joint Conference on Neural Networks
Year: 1990 | Conference Paper |
Cited by: Papers (5)
Hardware architectures for character recognition are discussed, and choices for possible circuits are outlined. An advanced (and working) reconfigurable neural-net chip that mixes analog and digital processing is described. It is found that different approaches to image recognition often lead to neural-net architectures that have limited connectivity and repeated use of the same set of weights. Th...Show More
Handwritten digit recognition: applications of neural network chips and automatic learning
Y. Le Cun;L.D. Jackel;B. Boser;J.S. Denker;H.P. Graf;I. Guyon;D. Henderson;R.E. Howard;W. Hubbard
Two novel methods for achieving handwritten digit recognition are described. The first method is based on a neural network chip that performs line thinning and feature extraction using local template matching. The second method is implemented on a digital signal processor and makes extensive use of constrained automatic learning. Experimental results obtained using isolated handwritten digits take...Show More
Comparing different neural network architectures for classifying handwritten digits
I. Guyon;I. Poujaud;L. Personnaz;G. Dreyfus;J. Denker;Y. Le Cun
International 1989 Joint Conference on Neural Networks
Year: 1989 | Conference Paper |
Cited by: Papers (20)
An evaluation is made of several neural network classifiers, comparing their performance on a typical problem, namely handwritten digit recognition. For this purpose, the authors use a database of handwritten digits, with relatively uniform handwriting styles. The authors propose a novel way of organizing the network architectures by training several small networks so as to deal separately with su...Show More
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