  Irregular text is widely used. However, it is considerably difficult to
recognize because of its various shapes and distorted patterns. In this paper,
we thus propose a multi-object rectified attention network (MORAN) for general
scene text recognition. The MORAN consists of a multi-object rectification
network and an attention-based sequence recognition network. The multi-object
rectification network is designed for rectifying images that contain irregular
text. It decreases the difficulty of recognition and enables the
attention-based sequence recognition network to more easily read irregular
text. It is trained in a weak supervision way, thus requiring only images and
corresponding text labels. The attention-based sequence recognition network
focuses on target characters and sequentially outputs the predictions.
Moreover, to improve the sensitivity of the attention-based sequence
recognition network, a fractional pickup method is proposed for an
attention-based decoder in the training phase. With the rectification
mechanism, the MORAN can read both regular and irregular scene text. Extensive
experiments on various benchmarks are conducted, which show that the MORAN
achieves state-of-the-art performance. The source code is available.
