부산대학교 도서관

The E13B font recognition method achieved fast and accurate recognition but had certain drawbacks. One of which was the lack of confidence information for character prediction. When the decision tree encounters an issue, a closer answer cannot be recovered, leading to the failure of the entire check recognition. Although this event is highly improbable, it could still occur. Therefore, we needed to continue optimizing the recognition process, and this study proposed a novel framework to address this issue. This study incorporated a model trained by a lightweight neural network to predict the result when the decision tree encounters an anomaly. This resolves the previous flaw of failing to provide a prediction when the tree encounters a problem. The trained model can also meet the computational requirements of embedded devices and complete the recognition of a character within 1.8 ms.