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Paper title:
Vehicle Front-Rear detection for license plate detection enhancement.

Description:
A deep learning-based network for detecting and classifying vehicle's front and rear. 
Received Best Paper Award out of 184 papers at 2019 MITA.
Open sourced,
https://github.com/hankerkuo/Vehicle-Front-Rear-Detection-for-License-Plate-Detection-Enhancement 

Publication date: Jun. 27, 2019.  
Publication description: 15th International Conference on Multimedia Information Technology and Applications(MITA), 2019

Best Paper Award at 2019 MITA

郭劭恆

碩士期間隸屬於Neural Networks and Robot Vision研究室。專精於基於深度學習的物件偵測。
碩論為完整建構一個與車牌辨識相關的物件偵測模型，包含訓練資料蒐集、資料標註與模型訓練，環境為Linux，利用工具為Python, keras, tensorflow, OpenCV。
專案利用Google提供的OCR API於Android平台上架構一個基於深度學習的文字辨識系統，環境為Android工業控制單板系統，使用工具為Java, Gradle, Android Studio。
精通英文與韓文，並熟悉閱讀各種框架的原文文檔，善於並樂於在國際化的環境中工作。

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