Primitive Representation Learning for Scene Text Recognition

Abstract

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.

References in corpus (22)

• Semi-Supervised Classification with Graph Convolutional Networks
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• ADADELTA: An Adaptive Learning Rate Method
• Inductive Representation Learning on Large Graphs
• Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
• Focusing Attention: Towards Accurate Text Recognition in Natural Images
• Synthetic Data for Text Localisation in Natural Images
• Multi-Oriented Text Detection with Fully Convolutional Networks
• What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis
• Recursive Recurrent Nets with Attention Modeling for OCR in the Wild
• Towards Accurate Scene Text Recognition with Semantic Reasoning Networks
• 2D Attentional Irregular Scene Text Recognizer
• Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
• Reading Scene Text in Deep Convolutional Sequences
• Scene Text Recognition from Two-Dimensional Perspective
• Edit Probability for Scene Text Recognition
• ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)
• NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition
• RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition
• Decoupled Attention Network for Text Recognition
• SCATTER: Selective Context Attentional Scene Text Recognizer
• Simultaneous Recognition of Horizontal and Vertical Text in Natural Images