Abstract
AbstractWe address the problem of offline handwritten diagram recognition. Recently, it has been shown that diagram symbols can be directly recognized with deep learning object detectors. However, object detectors are not able to recognize the diagram structure. We propose Arrow R-CNN, the first deep learning system for joint symbol and structure recognition in handwritten diagrams. Arrow R-CNN extends the Faster R-CNN object detector with an arrow head and tail keypoint predictor and a diagram-aware postprocessing method. We propose a network architecture and data augmentation methods targeted at small diagram datasets. Our diagram-aware postprocessing method addresses the insufficiencies of standard Faster R-CNN postprocessing. It reconstructs a diagram from a set of symbol detections and arrow keypoints. Arrow R-CNN improves state-of-the-art substantially: on a scanned flowchart dataset, we increase the rate of recognized diagrams from 37.7 to 78.6%.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Computer Vision and Pattern Recognition,Software
Reference41 articles.
1. Awal, A.M., Feng, G., Mouchère, H., Viard-Gaudin, C.: First experiments on a new online handwritten flowchart database. In: Document Recognition and Retrieval XVIII, p. 78740A (2011)
2. Bresler, M., Phan, T.V., Prusa, D., Nakagawa, M., Hlavác, V.: Recognition System for On-Line Sketched Diagrams. In: 2014 14th International Conference on Frontiers in Handwriting Recognition, pp. 563–568 (2014)
3. Bresler, M., Průša, D., Hlaváč, V.: Modeling flowchart structure recognition as a max-sum problem. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1215–1219 (2013)
4. Bresler, M., Průša, D., Hlaváč, V.: Simultaneous segmentation and recognition of graphical symbols using a composite descriptor. In: 18th Computer Vision Winter Workshop, vol. 13, pp. 16–23 (2013)
5. Bresler, M., Průša, D., Hlaváč, V.: Detection of arrows in on-line sketched diagrams using relative stroke positioning. In: 2015 IEEE Winter Conference on Applications of Computer Vision, pp. 610–617 (2015)
Cited by
16 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献