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Offline handwriting recognition is a well-known challenging task in the optical character recognition field due to the difficulty caused by various unconstrained handwriting styles and limited training data. In order to learn invariant feature representations for handwriting, we propose a novel method to incorporate pixel-level rectification into a CNN-and RNNbased recognizer. We also propose an adjacent output mixup method for RNN layer's training to improve the generalization ability of the recognizer, i.e., the previous output of an RNN layer is added to the current output with random weights. We additionally adopt a series of techniques including pre-training, data augmentation and language model to significantly expand the training data scale, and further analyze their contributions to the improvement in the model performance. The proposed method performs well on four public offline handwriting benchmarks, including the IAM, Rimes, IFN/ENIT and CASIA-HWDB datasets.

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