Keyword Spotting in Online Handwritten Documents Containing Text and Non-text Using BLSTM Neural Networks

Spotting keywords in handwritten documents without transcription is a valuable method as it allows one to search, index, and classify such documents. In this paper we show that keyword spotting based on bi-directional Long Short-Term Memory (BLSTM) recurrent neural nets can successfully be applied on online handwritten documents with non-text content. It even works without preprocessing steps such as text vs. non-text distinction and text line extraction. We also propose a modification that can improve the precision with little effort.