  Scene text recognition is a challenging task due to diverse variations of
text instances in natural scene images. Conventional methods based on
CNN-RNN-CTC or encoder-decoder with attention mechanism may not fully
investigate stable and efficient feature representations for multi-oriented
scene texts. In this paper, we propose a primitive representation learning
method that aims to exploit intrinsic representations of scene text images. We
model elements in feature maps as the nodes of an undirected graph. A pooling
aggregator and a weighted aggregator are proposed to learn primitive
representations, which are transformed into high-level visual text
representations by graph convolutional networks. A Primitive REpresentation
learning Network (PREN) is constructed to use the visual text representations
for parallel decoding. Furthermore, by integrating visual text representations
into an encoder-decoder model with the 2D attention mechanism, we propose a
framework called PREN2D to alleviate the misalignment problem in
attention-based methods. Experimental results on both English and Chinese scene
text recognition tasks demonstrate that PREN keeps a balance between accuracy
and efficiency, while PREN2D achieves state-of-the-art performance.
