본 논문에서는 한글, 영문, 숫자 및 특수 기호 4종의 글자들이 혼합된 필기체 이미지를 입력으로 하는 글자 인식 엔진의 구현 과정에 대하여 설명한다. 심층 학습 기반 신경망 모델 학습을 위한 대용량 데이타셋은 폰트 기반의 글자 이미지를 기반으로 데이타 증강 기법을 통하여 확보하였다. 글자 인식 엔진의 핵심부인 CNN 기반 심층신경망 모델은 고성능 GPU가 장착된 데스크톱에서 학습하였다. 학습이 완료된 신경망 모델의 인식 정확도는Top-1 accuracy 0.98의 성능을 보였다. 데스크톱에서 학습된 모델은 상대적으로 컴퓨팅 리소스가 한정된 임베디드시스템상에서 실행할 수 있도록 추론 기능만을 포함하는 심층 신경망 모델로 이식하였다. 임베디드 시스템에 이식된 심층 신경망 모델은 낱글자 인식 및 간단한 양식 내의 단어 인식을 위한 응용 프로그램에서 글자 인식 엔진으로 활용되었다.
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