1
|
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%.
Collapse
|
2
|
Automatic room information retrieval and classification from floor plan using linear regression model. INT J DOC ANAL RECOG 2020. [DOI: 10.1007/s10032-020-00357-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
3
|
Making metric learning algorithms invariant to transformations using a projection metric on Grassmann manifolds. INT J MACH LEARN CYB 2019. [DOI: 10.1007/s13042-019-00927-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
4
|
Sharma D, Gupta N, Chattopadhyay C, Mehta S. A novel feature transform framework using deep neural network for multimodal floor plan retrieval. INT J DOC ANAL RECOG 2019. [DOI: 10.1007/s10032-019-00340-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
5
|
Abstract
In this paper, we address a new scheme for symbol retrieval based on bag-of-relations (BoRs) which are computed between extracted visual primitives (e.g. circle and corner). Our features consist of pairwise spatial relations from all possible combinations of individual visual primitives. The key characteristic of the overall process is to use topological relation information indexed in BoRs and use this for recognition. As a consequence, directional relation matching takes place only with those candidates having similar topological configurations. A comprehensive study is made by using several different well-known datasets such as GREC, FRESH and SESYD, and includes a comparison with state-of-the-art descriptors. Experiments provide interesting results on symbol spotting and other user-friendly symbol retrieval applications.
Collapse
Affiliation(s)
- K. C. SANTOSH
- US National Library of Medicine, National Institutes of Health, 8600 Rockville, Bethesda, MD 20894, USA
| | - LAURENT WENDLING
- SIP — LIPADE, Université Paris Descartes (Paris V), 45, rue des Saints-Pères, 75270 Paris Cedex 06, France
| | - BART LAMIROY
- Université de Lorraine — LORIA (UMR-7503) Campus Scientifique, BP 239 — 54506 Vandoeuvre-les-Nancy Cedex, France
| |
Collapse
|
6
|
de las Heras LP, Ahmed S, Liwicki M, Valveny E, Sánchez G. Statistical segmentation and structural recognition for floor plan interpretation. INT J DOC ANAL RECOG 2013. [DOI: 10.1007/s10032-013-0215-2] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|