Authors: Youngmin Baek, Seung Shin, Jeonghun Baek, Bado Lee, Chae Young Lee, and Hwalsuk Lee
Description: We propose a novel end-to-end text detection and recognition method called CRAFTS (Character Region Awareness For Text Spotting). CRAFTS is an end-to-end trainable network capable of detecting and recognizing multiple languages. The detection branch estimates the position and the orientation of the texts in the input image. The recognition is conducted with an attention-based decoder, utilizing the pooled text area features from the detection branch. The script identification is performed by identifying the most frequent language occurrences of the characters in the text. The text detector effectively detects text area by exploring each of the character regions and the affinities between the characters. To overcome the lack of individual character level annotations, our detection framework exploits the pseudo character-level bounding boxes in a weakly-supervised manner. The pseudo character-level bounding boxes are acquired by inferencing the learned interim model.
Clova AI OCR Team, NAVER/LINE Corp.