OCR 엔진 기반 분류기 애드온 결합을 통한 이미지 내부 텍스트 인식 성능 향상
Scene Text Recognition Performance Improvement through an Add-on of an OCR based Classifier
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초록
일상 환경에서 동작하는 자율 에이전트를 구현하기 위해서는 이미지나 객체에 존재하는 텍스트를 인식하는 기능이 필수적이다. 주어진 이미지에 입력 변환, 특성 인식, 워드 예측을 적용하여 인식된 텍스트에 존재하는 워드를 출력하는 과정에 다양한 딥러닝 모델이 활용되고 있으며, 딥뉴럴넷의 놀라운 객체 인식 능력으로 인식 성능이 매우 향상되었지만 실제 환경에 적용하기에는 아직 부족한 점이 많다. 본 논문에서는 인식 성능 향상을 위하여 텍스트 존재 영역 감지, 텍스트 인식, 워드 예측의 파이프라인에 OCR 엔진과 분류기로 구성된 애드온을 추가하여 기존 파이프라인이 인식하지 못한 텍스트의 인식을 시도하는 접근법을 제안한다. IC13, IC15의 데이터 셋에 제안 방법을 적용한 결과, 문자 단위에서 기존 파이프라인이 인식하는데실패한 문자의 최대 10.92%를 인식함을 확인하였다.
Abstract
An autonomous agent for real world should be able to recognize text in scenes. With the advancement of deep learning,various DNN models have been utilized for transformation, feature extraction, and predictions. However, the existingstate-of-the art STR (Scene Text Recognition) engines do not achieve the performance required for real world applications. In this paper, we introduce a performance-improvement method through an add-on composed of an OCR (OpticalCharacter Recognition) engine and a classifier for STR engines. On instances from IC13 and IC15 datasets which a STRengine failed to recognize, our method recognizes 10.92% of unrecognized characters.
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