Fast Korean Text Detection and Recognition in Traffic Guide Signs
Hyunjun Eun, Jonghee Kim, Jinsu Kim, Changick Kim · 2018
In this paper, we propose a fast method based on deep neural networks to detect and recognize Korean characters in traffic guide signs. To detect character candidates quickly, we first employ a region proposal network (RPN) which is in this paper ResNet-18, being relatively shallow. We also apply the Inception architecture to residual blocks for reducing parameters of the network. After character candidates are detected, we classify them into 709 Korean characters by using a classification network (CLSN). Similar to the RPN, our CLSN consists of residual blocks with the Inception architecture. In experiments, we achieved 97.69 % of accuracy at 5.9fps on both detection and recognition of Korean characters in traffic guide signs.