Algorithms for clustering data
Algorithms for clustering data
The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design of a neural network character recognizer for a touch terminal
Pattern Recognition
Large Vocabulary Recognition of On-Line Handwritten Cursive Words
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Normalization of Handwritten Characters Using Global/Local Affine Transformation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining High-Level Features with Sequential Local Features for On-Line Handwriting Recognition
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
Serial classifier combination for handwritten word recognition
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Learning Prototypes for On-Line Handwritten Digits
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
NPen/sup ++/: a writer independent, large vocabulary on-line cursive handwriting recognition system
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Online handwriting recognition using multiple pattern class models
Online handwriting recognition using multiple pattern class models
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Elastic Structural Matching for On-Line Handwritten Alphanumeric Character Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A New Hybrid Approach to Large Vocabulary Cursive Handwriting Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Writer dependent recognition of on-line unconstrained handwriting
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Duration modeling results for an on-line handwriting recognizer
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
MathPad2: a system for the creation and exploration of mathematical sketches
ACM SIGGRAPH 2004 Papers
Evaluation of an on-line adaptive gesture interface with command prediction
GI '05 Proceedings of Graphics Interface 2005
On-line Writer Adaptation for Handwriting Recognition using Fuzzy Inference Systems
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Minimum Classification Error Training for Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personalized handwriting recognition via biased regularization
ICML '06 Proceedings of the 23rd international conference on Machine learning
MathPad2: a system for the creation and exploration of mathematical sketches
ACM SIGGRAPH 2006 Courses
MathPad2: a system for the creation and exploration of mathematical sketches
ACM SIGGRAPH 2007 courses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving word-recognizers using an interactive lexicon with active and passive words
Proceedings of the 13th international conference on Intelligent user interfaces
Retrieval of online handwriting by synthesis and matching
Pattern Recognition
Estimating HMM Parameters Using Particle Swarm Optimisation
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Unsupervised writer adaptation of whole-word HMMs with application to word-spotting
Pattern Recognition Letters
Semi-automatic training sets acquisition for handwriting recognition
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
An adaptive circular (text) input for handheld devices
Proceedings of the 12th International Conference on Computer Systems and Technologies
Data clustering: a user’s dilemma
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Similarity-based training set acquisition for continuous handwriting recognition
Information Sciences: an International Journal
Lightweight user-adaptive handwriting recognizer for resource constrained handheld devices
Proceeding of the workshop on Document Analysis and Recognition
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Writer-adaptation is the process of converting a writer-independent handwriting recognition system, which models the characteristics of a large group of writers, into a writer-dependent system, which is tuned for a particular writer. This adaptation has the potential of greatly increasing recognition accuracies, provided adequate models can be constructed for a particular writer. The limited amount of data a writer is willing to provide during the training phase constrains the complexity of these models. We show how the appropriate use of writer-independent models is important for the adaptation process. Our approach to writer-adaptation makes use of writer-independent writing style models (called lexemes), to identify the styles present in a particular writer's training data. These models are then updated using the writer's data. Lexemes that are present in the writer's data, but for which an inadequate number of training examples is available, are replaced with the writer-independent models. We demonstrate the feasibility of this approach on both isolated handwritten character recognition and unconstrained word recognition tasks. Our results show an average reduction in error rate of 16.3 percent for lowercase characters as compared against representing each of the writer's character classes with a single model. In addition, an average error rate reduction of 9.2 percent is shown on handwritten words using only a small amount of data for adaptation.