NRTR: A NoRecurrence SequencetoSequence Model for Scene Text Recognition Scene text recognition has attracted a great many researches due to its importance to various applications. Existing methods mainly adopt recurrence or convolution based networks. Though have obtained good performance, these methods still suffer from two limitations: slow training speed due to the internal recurrence of RNNs, and high complexity due to stacked convolutional layers for longterm feature extraction. This paper, for the first time, proposes a norecurrence sequencetosequence text recognizer, named NRTR, that dispenses with recurrences and convolutions entirely. NRTR follows the encoderdecoder paradigm, where the encoder uses stacked selfattention to extract image features, and the decoder applies stacked selfattention to recognize texts based on encoder output. NRTR relies solely on selfattention mechanism thus could be trained with more parallelization and less complexity. Considering scene image has large variation in text and background, we further design a modalitytransform block to effectively transform 2D input images to 1D sequences, combined with the encoder to extract more discriminative features. NRTR achieves stateoftheart or highly competitive performance on both regular and irregular benchmarks, while requires only a small fraction of training time compared to the best model from the literature (at least 8 times faster).

Evaluating SequencetoSequence Models for Handwritten Text Recognition Encoderdecoder models have become an effective approach for sequence learning tasks like machine translation, image captioning and speech recognition, but have yet to show competitive results for handwritten text recognition. To this end, we propose an attentionbased sequencetosequence model. It combines a convolutional neural network as a generic feature extractor with a recurrent neural network to encode both the visual information, as well as the temporal context between characters in the input image, and uses a separate recurrent neural network to decode the actual character sequence. We make experimental comparisons between various attention mechanisms and positional encodings, in order to find an appropriate alignment between the input and output sequence. The model can be trained endtoend and the optional integration of a hybrid loss allows the encoder to retain an interpretable and usable output, if desired. We achieve competitive results on the IAM and ICFHR2016 READ data sets compared to the stateoftheart without the use of a language model, and we significantly improve over any recent sequencetosequence approaches.