Poster
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks
Alex Graves · Santiago Fernandez · Marcus Liwicki · Horst Bunke · Jürgen Schmidhuber

Wed Dec 5th 10:30 -- 10:40 AM @ None #None

On-line handwriting recognition is unusual among sequence labelling tasks in that the underlying generator of the observed data, i.e. the movement of the pen, is recorded directly. However, the raw data can be difficult to interpret because each letter is spread over many pen locations. As a consequence, sophisticated pre-processing is required to obtain inputs suitable for conventional sequence labelling algorithms, such as HMMs. In this paper we describe a system capable of directly transcribing raw on-line handwriting data. The system consists of a recurrent neural network trained for sequence labelling, combined with a probabilistic language model. In experiments on an unconstrained on-line database, we record excellent results using either raw or pre-processed data, well outperforming a benchmark HMM in both cases.

Author Information

Alex Graves (Google DeepMind)

Main contributions to neural networks include the Connectionist Temporal Classification training algorithm (widely used for speech, handwriting and gesture recognition, e.g. by Google voice search), a type of differentiable attention for RNNs (originally for handwriting generation, now a standard tool in computer vision, machine translation and elsewhere), stochastic gradient variational inference, and Neural Turing Machines. He works at Google Deep Mind.

Santiago Fernandez (IDSIA)
Marcus Liwicki (Institute of Computer Science & Applied Math, University of Bern, Switzerland)
Horst Bunke (Institute of Computer Science & Applied Math, University of Bern, Switzerland)
Jürgen Schmidhuber (Swiss AI Lab, IDSIA (USI & SUPSI) - NNAISENSE)

Since age 15, his main goal has been to build an Artificial Intelligence smarter than himself, then retire. The Deep Learning Artificial Neural Networks developed since 1991 by his research groups have revolutionised handwriting recognition, speech recognition, machine translation, image captioning, and are now available to billions of users through Google, Microsoft, IBM, Baidu, and many other companies (DeepMind also was heavily influenced by his lab). His team's Deep Learners were the first to win object detection and image segmentation contests, and achieved the world's first superhuman visual classification results, winning nine international competitions in machine learning & pattern recognition. His formal theory of fun & creativity & curiosity explains art, science, music, and humor. He has published 333 papers, earned 7 best paper/best video awards, the 2013 Helmholtz Award of the International Neural Networks Society, and the 2016 IEEE Neural Networks Pioneer Award. He is also president of NNAISENSE, which aims at building the first practical general purpose AI.

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