  Scene text recognition (STR) involves the task of reading text in cropped
images of natural scenes. Conventional models in STR employ convolutional
neural network (CNN) followed by recurrent neural network in an encoder-decoder
framework. In recent times, the transformer architecture is being widely
adopted in STR as it shows strong capability in capturing long-term dependency
which appears to be prominent in scene text images. Many researchers utilized
transformer as part of a hybrid CNN-transformer encoder, often followed by a
transformer decoder. However, such methods only make use of the long-term
dependency mid-way through the encoding process. Although the vision
transformer (ViT) is able to capture such dependency at an early stage, its
utilization remains largely unexploited in STR. This work proposes the use of a
transformer-only model as a simple baseline which outperforms hybrid
CNN-transformer models. Furthermore, two key areas for improvement were
identified. Firstly, the first decoded character has the lowest prediction
accuracy. Secondly, images of different original aspect ratios react
differently to the patch resolutions while ViT only employ one fixed patch
resolution. To explore these areas, Pure Transformer with Integrated Experts
(PTIE) is proposed. PTIE is a transformer model that can process multiple patch
resolutions and decode in both the original and reverse character orders. It is
examined on 7 commonly used benchmarks and compared with over 20
state-of-the-art methods. The experimental results show that the proposed
method outperforms them and obtains state-of-the-art results in most
benchmarks.
