A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks
Marcus Liwicki, Alex M Graves, Horst Bunke, Jürgen Schmidhuber · BORIS (University Library Bern) · 2007
In this paper we introduce a new connectionist approach to on-line handwriting recognition and address in particular the problem of recognizing handwritten whiteboard notes.The approach uses a bidirectional recurrent neural network with long short-term memory blocks.We use a recently introduced objective function, known as Connectionist Temporal Classification (CTC), that directly trains the network to label unsegmented sequence data.Our new system achieves a word recognition rate of 74.0 %, compared with 65.4 % using a previously developed HMMbased recognition system.