Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks

Offline handwriting recognition--the transcription of images of handwritten text--is an interesting task, in that it combines computer vision with sequence learning. In most systems the two elements are handled separately, with sophisticated preprocessing techniques used to extract the image features and sequential models such as HMMs used to provide the transcriptions. By combining two recent innovations in neural networks--multidimensional recurrent neural networks and connectionist temporal classification--this paper introduces a globally trained offline handwriting recogniser that takes raw pixel data as input. Unlike competing systems, it does not require any alphabet specific preprocessing, and can therefore be used unchanged for any language. Evidence of its generality and power is provided by data from a recent international Arabic recognition competition, where it

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Alex Graves, Jürgen Schmidhuber
• Keywords: Information Technology, NIPS 2008, Offline Handwriting Recogniser, Recurrent Neural Networks, Sophisticated Preprocessing Techniques