Techniques for automatically correcting words in text
ACM Computing Surveys (CSUR)
Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
Substitution Deciphering Based on HMMs with Applications to Compressed Document Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhancing Degraded Document Images via Bitmap Clustering and Averaging
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Bootstrapping Text Recognition from Stop Words
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Visual inter-word relations and their use in OCR postprocessing
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Character Recognition by Adaptive Statistical Similarity
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A generative probabilistic OCR model for NLP applications
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Context-Sensitive Error Correction: Using Topic Models to Improve OCR
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Scene Text Recognition Using Similarity and a Lexicon with Sparse Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Artificial Intelligence Research
Learning on the Fly: Font-Free Approaches to Difficult OCR Problems
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Meta-Recognition: The Theory and Practice of Recognition Score Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The l1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted l1-penalized estimators for convex loss functions of a general form, including the generalized linear models. We study the estimation, prediction, selection and sparsity properties of the weighted l1-penalized estimator in sparse, high-dimensional settings where the number of predictors p can be much larger than the sample size n. Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results.