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Offline Arabic Handwriting Recognition with Multidimensional Recurrent Neural Networks

Offline handwriting recognition is usually performed by first extracting a sequence of features from the image, then using either a hidden Markov model (HMM) [9] or an HMM / neural network hybrid [10] to transcribe the features. However a system trained directly on pixel data has several potential advantages. One is that defining input features suitable for an HMM requires considerable time and expertise. Furthermore, the features must be redesigned for every different alphabet. In contrast, a system trained on raw images can be applied with equal ease to, for example, Arabic and English. Another potential benefit is that using raw data allows the visual and sequential aspects of handwriting recognition to be learned together, rather than treated as two separate problems. This kind of ‘end-to-end’ training is often beneficial for machine learning algorithms, since it allows them more freedom to adapt to the task [13]. Furthermore, recent results suggest that recurrent neural networks (RNNs) may be preferable to HMMs for sequence labelling tasks such as speech [5] and online handwriting recognition [6]. One possible reason for this is that RNNs are trained discriminatively, whereas HMMs are generative. Although generative approaches offer more insight into the data, discriminative methods tend to perform better at tasks such as classification and labelling, at least when large amounts of data are available [15]. Indeed much work has been in recent years to introduce discriminative training to HMMs [11]. Another important difference is that RNNs, unlike HMMs, do not assume successive data points to be conditionally independent given some discrete internal state, which is often unrealistic for cursive handwriting. This chapter will describe an offline handwriting recognition system based on recurrent neural networks. The system is trained directly on raw images, with no manual feature extraction. It won several prizes at the 2009 International Conference on Document Analysis and Recognition, including first place in the offline Arabic handwriting recognition competition [14].