Wacnet: Word Segmentation Guided Characters Aggregation Net for Scene Text Spotting With Arbitrary Shapes

In this paper, we propose an end-to-end trainable framework for scene text spotting which can handle text with arbitrary shapes. The proposed framework is called Word Segmentation Guided Characters Aggregation Net (WAC-Net), which consists of a shared convolutional backbone and two task-specific subnetworks. One subnetwork does word-level instance-aware segmentation (WSN) and the other does char-level detection and recognition (CDRN). The entire framework segments each word instance while detects and recognizes each character in one single forward pass. These two subnetworks are jointly trained by multi-task learning. At the inference stage, characters are aggregated into words guided by word instance segmentation results. Experiments are conducted on two datasets with arbitrary shapes, and the results demonstrate the effectiveness of the proposed method.