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36
Recognition of Cursive Roman Handwriting - Past, Present and Future
- In Proc. 7th Int. Conf. on Document Analysis and Recognition
, 2003
"... This paper review the state of the art in o#-line Roman cursive han dw iting recognition. The input provided to an o#-line han iting recognition system is an image of a digit, aw ord, or - more generally - some text, and the system produces, as output, an ASCII transcription of the input. This taski ..."
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Cited by 37 (10 self)
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This paper review the state of the art in o#-line Roman cursive han dw iting recognition. The input provided to an o#-line han iting recognition system is an image of a digit, aw ord, or - more generally - some text, and the system produces, as output, an ASCII transcription of the input. This taskinvolves a number of processing steps, some of w ich are quite di#cult. Typically, preprocessing, normalization, feature extraction, classification, and postprocessing operations are required. We'll survey the state of the art, analyze recent trends, and try to identify challenges for future research in this field.
2004a). Effects of classifier structures and training regimes on integrated segmentation and recognition of handwritten numeral strings
- IEEE Trans on Patterns Analysis and Machine Intelligence
"... Abstract—In integrated segmentation and recognition of character strings, the underlying classifier is trained to be resistant to noncharacters. We evaluate the performance of state-of-the-art pattern classifiers of this kind. First, we build a baseline numeral string recognition system with simple ..."
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Cited by 20 (4 self)
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Abstract—In integrated segmentation and recognition of character strings, the underlying classifier is trained to be resistant to noncharacters. We evaluate the performance of state-of-the-art pattern classifiers of this kind. First, we build a baseline numeral string recognition system with simple but effective presegmentation. The classification scores of the candidate patterns generated by presegmentation are combined to evaluate the segmentation paths and the optimal path is found using the beam search strategy. Three neural classifiers, two discriminative density models, and two support vector classifiers are evaluated. Each classifier has some variations depending on the training strategy: maximum likelihood, discriminative learning both with and without noncharacter samples. The string recognition performances are evaluated on the numeral string images of the NIST Special Database 19 and the zipcode images of the CEDAR CDROM-1. The results show that noncharacter training is crucial for neural classifiers and support vector classifiers, whereas, for the discriminative density models, the regularization of parameters is important. The string recognition results compare favorably to the best ones reported in the literature though we totally ignored the geometric context. The best results were obtained using a support vector classifier, but the neural classifiers and discriminative density models show better trade-off between accuracy and computational overhead. Index Terms—Numeral string recognition, integrated segmentation and recognition, noncharacter resistance, character classification, neural classifiers, discriminative density models, support vector classifiers. æ 1
Support Vector Machines for Handwritten Numerical String Recognition
- Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition (IWFHR-9
, 2004
"... In this paper we discuss the use of SVMs to recognize handwritten numerical strings. Such a problem is more complex than recognizing isolated digits since one must deal with problems such as segmentation, overlapping, unknown number of digits, etc. In order to perform our experiments, we have used a ..."
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Cited by 13 (2 self)
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In this paper we discuss the use of SVMs to recognize handwritten numerical strings. Such a problem is more complex than recognizing isolated digits since one must deal with problems such as segmentation, overlapping, unknown number of digits, etc. In order to perform our experiments, we have used a segmentation-based recognition system using heuristic over-segmentation. The contribution of this paper is twofold. Firstly, we demonstrate by experimentation that SVMs improve the overall recognition rates. Secondly, we observe that SVMs deal with outliers such as over- and under-segmentation better than multi-layer perceptron neural networks.
The interaction between classification and reject performance for distance-based reject-option classifiers
, 2005
"... Consider the class of problems in which a target class is well-defined, and an outlier class is ill-defined. In these cases new outlier classes can appear, or the class-conditional distribution of the outlier class itself may be poorly sampled. A strategy to deal with this problem involves a two-sta ..."
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Cited by 11 (1 self)
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Consider the class of problems in which a target class is well-defined, and an outlier class is ill-defined. In these cases new outlier classes can appear, or the class-conditional distribution of the outlier class itself may be poorly sampled. A strategy to deal with this problem involves a two-stage classifier, in which one stage is designed to perform discrimination between known classes, and the other stage encloses known data to protect against changing conditions. The two stages are, however, interrelated, implying that optimising one may compromise the other. In this paper the relation between the two stages is studied within an ROC analysis framework. We show how the operating characteristics can be used for both model selection, and in aiding in the choice of the reject threshold. An analytic study on a controlled experiment is performed, followed by some experiments on real-world datasets with the distance-based rejectoption classifier.
Education Association, Standards for Technological Literacy: Content for the Study of Technology
- Problems in Neural Networks and Learning in Document Analysis and Recognition. First IAPR TC3 NNLDAR Workshop, Seoul, Korea
, 2000
"... Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. This class of methods includes statistical methods, artificial neural networks, support vector machines, ..."
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Cited by 11 (1 self)
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Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. This class of methods includes statistical methods, artificial neural networks, support vector machines, multiple classifier combination, etc. In this paper, we discuss the characteristics of the classification methods that have been successfully applied to character recognition, and show the remaining problems that can be potentially solved by learning methods. 1.
Adaptive Classifiers for Multi-Source OCR
- Int’l J. Document Analysis and Recognition
, 2003
"... When patterns occur in large groups generated by a single source (style consistent test data), the statistics of the test data differ from those of the training data which consists of patterns from all sources. We present a Gaussian model for continuously distributed sources under which we develop ..."
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Cited by 10 (7 self)
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When patterns occur in large groups generated by a single source (style consistent test data), the statistics of the test data differ from those of the training data which consists of patterns from all sources. We present a Gaussian model for continuously distributed sources under which we develop adaptive classifiers that specialize to the statistics of styleconsistent test data. On NIST handwritten digit data, the adaptive classifiers reduce the error rate by more than 50% operating on one writer (#10 samples/class) at a time.
Style context with second-order statistics
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2005
"... Abstract—Patterns often occur as homogeneous groups or fields generated by the same source. In multisource recognition problems, such isogeny induces statistical dependencies between patterns (termed style context). We model these dependencies by secondorder statistics and formulate the optimal clas ..."
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Cited by 7 (2 self)
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Abstract—Patterns often occur as homogeneous groups or fields generated by the same source. In multisource recognition problems, such isogeny induces statistical dependencies between patterns (termed style context). We model these dependencies by secondorder statistics and formulate the optimal classifier for normally distributed styles. We show that model parameters estimated only from pairs of classes suffice to train classifiers for any test field length. Although computationally expensive, the style-conscious classifier reduces the field error rate by up to 20 percent on quadruples of handwritten digits from standard NIST data sets. Index Terms—Interpattern feature dependence, writer consistency, continuous styles, quadratic discriminant classifier. 1
Recognition of Handwritten Chinese Characters by Combining Regularization, Fisher's Discriminant and Distorted Sample Generation
- 10TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION
, 2009
"... The problem of offline handwritten Chinese character recognition has been extensively studied by many researchers and very high recognition rates have been reported. In this paper, we propose to further boost the recognition rate by incorporating a distortion model that artificially generates a huge ..."
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Cited by 5 (1 self)
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The problem of offline handwritten Chinese character recognition has been extensively studied by many researchers and very high recognition rates have been reported. In this paper, we propose to further boost the recognition rate by incorporating a distortion model that artificially generates a huge number of virtual training samples from existing ones. We achieve a record high recognition rate of 99.46% on the ETL-9B database. Traditionally, when the dimension of the feature vector is high and the number of training samples is not sufficient, the remedies are to (i) regularize the class covariance matrices in the discriminant functions, (ii) employ Fisher's dimension reduction technique to reduce the feature dimension, and (iii) generate a huge number of virtual training samples from existing ones. The second contribution of this paper is the investigation of the relative effectiveness of these three methods for boosting the recognition rate.
Syleconstrained quadratic field classifiers
- Rensselaer Polytechnic Institute PhD Dissertation
, 2002
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Towards a Ptolemaic Model for OCR
"... In style-constrained classification often there are only a few samples of each style and class, and the correspondences between styles in the training set and the test set are unknown. To avoid gross misestimates of the classifier parameters it is therefore important to model the pattern distributio ..."
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Cited by 4 (3 self)
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In style-constrained classification often there are only a few samples of each style and class, and the correspondences between styles in the training set and the test set are unknown. To avoid gross misestimates of the classifier parameters it is therefore important to model the pattern distributions accurately. We offer empirical evidence for intuitively appealing assumptions, in feature spaces appropriate for symbolic patterns, for (1) tetrahedral configurations of class means that suggests linear style-adaptive classification, (2) improved estimates of classification boundaries by taking into account the asymmetric configuration of the patterns with respect to the directions toward other classes, and (3) pattern-correlated style variability.
