Symbol detection in online handwritten graphics using Faster R-CNN
This work addresses the need for general symbol detection methods across different types of handwritten graphics, which is incremental as it applies an existing algorithm to new data.
The paper tackled the problem of symbol detection in online handwritten graphics by evaluating the Faster R-CNN object detection algorithm as a general method, achieving effective results on flowchart and mathematical expression datasets.
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.