On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Finding Perceptually Closed Paths in Sketches and Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure in On-line Documents
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Discerning Structure from Freeform Handwritten Notes
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinguishing Text from Graphics in On-Line Handwritten Ink
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Learning to Parse Hierarchical Lists and Outlines Using Conditional Random Fields
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Mode detection in on-line pen drawing and handwriting recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Table Detection in Online Ink Notes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text/Non-text Ink Stroke Classification in Japanese Handwriting Based on Markov Random Fields
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Categorization of Digital Ink Elements Using Spectral Features
Graphics Recognition. Recent Advances and New Opportunities
A Novel Connectionist System for Unconstrained Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust approach to text line grouping in online handwritten Japanese documents
Pattern Recognition
Iconic and multi-stroke gesture recognition
Pattern Recognition
Using entropy to distinguish shape versus text in hand-drawn diagrams
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
IAMonDo-database: an online handwritten document database with non-uniform contents
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
MCS for Online Mode Detection: Evaluation on Pen-Enabled Multi-touch Interfaces
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
CROHME2011: Competition on Recognition of Online Handwritten Mathematical Expressions
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Fuzzy Relative Positioning Templates for Symbol Recognition
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
A comparative study of energy minimization methods for markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
HBF49 feature set: A first unified baseline for online symbol recognition
Pattern Recognition
Mode Detection in Online Handwritten Documents Using BLSTM Neural Networks
ICFHR '12 Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition
Local Feature Based Online Mode Detection with Recurrent Neural Networks
ICFHR '12 Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition
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Analysing online handwritten notes is a challenging problem because of the content heterogeneity and the lack of prior knowledge, as users are free to compose documents that mix text, drawings, tables or diagrams. The task of separating text from non-text strokes is of crucial importance towards automated interpretation and indexing of these documents, but solving this problem requires a careful modelling of contextual information, such as the spatial and temporal relationships between strokes. In this work, we present a comprehensive study of contextual information modelling for text/non-text stroke classification in online handwritten documents. Formulating the problem with a conditional random field permits to integrate and combine multiple sources of context, such as several types of spatial and temporal interactions. Experimental results on a publicly available database of freely hand-drawn documents demonstrate the superiority of our approach and the benefit of contextual information combination for solving text/non-text classification.