Combination of global and local contexts for text/non-text classification in heterogeneous online handwritten documents

作者：

Highlights：

• We present a method to combine global and local contexts for text/non-text classification in online handwritten documents.

• Global context refers to the feature vector sequence of an entire document and local context refers to the prediction of directly adjacent strokes.

• Global and local contexts are integrated by using a simple marginal distribution and basic combination rules.

• We propose multiple classifier combination strategies for combining global and local contexts to improve classification accuracy.

摘要

•We present a method to combine global and local contexts for text/non-text classification in online handwritten documents.•Global context refers to the feature vector sequence of an entire document and local context refers to the prediction of directly adjacent strokes.•Global and local contexts are integrated by using a simple marginal distribution and basic combination rules.•We propose multiple classifier combination strategies for combining global and local contexts to improve classification accuracy.

论文关键词：Text/non-text classification,Ink stroke classification,Online handwritten documents,Heterogeneous documents,Recurrent neural networks,Long short-term memory

论文评审过程：Received 26 September 2014, Revised 14 April 2015, Accepted 24 July 2015, Available online 4 August 2015, Version of Record 27 November 2015.

论文官网地址：https://doi.org/10.1016/j.patcog.2015.07.012