Symbol detection in online handwritten graphics using Faster R-CNN

Author(s)

Julca-Aguilar, Frank D. ; Hirata, Nina S. T. ; IEEE

• Document type: Journal article
• Source: 2018 13TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS (DAS); v. N/A, p. 6-pg., 2018-01-01.

Abstract

Symbol detection techniques in online handwritten graphics (e.g. diagrams and mathematical expressions) consist of methods specifically designed for a single graphic type. In this work, we evaluate the Faster R-CNN object detection algorithm as a general method for detection of symbols in handwritten graphics. We evaluate different configurations of the Faster R-CNN method, and point out issues relative to the handwritten nature of the data. Considering the online recognition context, we evaluate efficiency and accuracy trade-offs of using Deep Neural Networks of different complexities as feature extractors. We evaluate the method on publicly available flowchart and mathematical expression (CROHME-2016) datasets. Results show that Faster R-CNN can be effectively used on both datasets, enabling the possibility of developing general methods for symbol detection, and furthermore, general graphic understanding methods that could be built on top of the algorithm. (AU)

• FAPESP's process: 15/17741-9 - Combination of local and global features in image operator learning
• Grantee: Nina Sumiko Tomita Hirata
• Support Opportunities: Regular Research Grants