level2-data-annotation_cv-level2-cv-15 created by GitHub Classroom

Overview

[AI Tech 3기 Level2 P Stage] 글자 검출 대회

image

팀원 소개

김규리_T3016 박정현_T3094 석진혁_T3109 손정균_T3111 이현진_T3174 임종현_T3182

Overview

OCR (Optimal Character Recognition) 기술은 사람이 직접 쓰거나 이미지 속에 있는 문자를 얻은 다음 이를 컴퓨터가 인식할 수 있도록 하는 기술로, 컴퓨터 비전 분야에서 현재 널리 쓰이는 대표적인 기술 중 하나입니다.

OCR task는 글자 검출 (text detection), 글자 인식 (text recognition), 정렬기 (Serializer) 등의 모듈로 이루어져 있는데 본 대회는 글자 검출 (text detection)만을 해결하게 됩니다.

데이터를 구성하고 활용하는 방법에 집중하는 것을 장려하는 취지에서, 제공되는 베이스 코드 중 모델과 관련한 부분을 변경하는 것이 금지되어 있습니다. 데이터 수집과 preprocessing, data augmentation 그리고 optimizer, learning scheduler 등 최적화 방식을 변경할 수 있습니다.

  • Input : 글자가 포함된 전체 이미지
  • Output : bbox 좌표가 포함된 UFO Format

평가방법

  • DetEval

    이미지 레벨에서 정답 박스가 여러개 존재하고, 예측한 박스가 여러개가 있을 경우, 박스끼리의 다중 매칭을 허용하여 점수를 주는 평가방법 중 하나 입니다

    1. 모든 정답/예측박스들에 대해서 Area Recall, Area Precision을 미리 계산해냅니다.

    2. 모든 정답 박스와 예측 박스를 순회하면서, 매칭이 되었는지 판단하여 박스 레벨로 정답 여부를 측정합니다.

    3. 모든 이미지에 대하여 Recall, Precision을 구한 이후, 최종 F1-Score은 모든 이미지 레벨에서 측정 값의 평균으로 측정됩니다.

      image

Final Score  🏅

  • Public : f1 0.6897 → Private f1 : 0.6751
  • Public : 11위/19팀 → Private : 9위/19팀

image

Archive contents

template
├──code
│  ├──augmentation.py
│  ├──convert_mlt.py
│  ├──dataset.py
│  ├──deteval.py
│  ├──east_dataset.py
│  ├──inference.py
│  ├──loss.py
│  ├──model.py
│  └──train.py
└──input
   └──ICDAR2017_Korean
		  └──data
			  	├──images
		      └──ufo
			        ├──train.json
							└──val.json

Dataset

  • ICDAR MLT17 Korean : 536 images ⊆ ICDAR MLT17 : 7,200 images

  • ICDAR MLT19 : 10,000 images

  • ICAR ArT : 5,603 images

Experiment

Results

dataset 데이터 수 LB score (public→private) Recall Precision
01 ICDAR17_Korean 536 0.4469 → 0.4732 0.3580 → 0.3803 0.5944 → 0.6264
02 Camper (폴리곤 수정 전) 1288 0.4543 → 0.5282 0.3627 → 0.4349 0.6077 → 0.6727
03 Camper (폴리곤 수정 후) 1288 0.4644 → 0.5298 0.3491 → 0.4294 0.6936 → 0.6913
04 ICDAR17_Korean + Camper 1824 0.4447 → 0.5155 0.3471 → 0.4129 0.6183 → 0.6858
05 ICDAR17(859) 859 0.5435 → 0.5704 0.4510 → 0.4713 0.6837 → 0.7222
06 ICDAR17_MLT 7200 0.6749 → 0.6751 0.5877 → 0.5887 0.7927 → 0.7912
07 ICDAR19+ArT 약 15000 0.6344 → 0.6404 0.5489 → 0.5607 0.7514 → 0.7465

Requirements

pip install -r requirements.txt

UFO Format으로 변환

python convert_mlt.py

SRC_DATASET_DIR = {변환 전 data 경로}

DST_DATASET_DIR = {변환 된 data 경로}

UFO Format ****

File Name
    ├── img_h
    ├── img_w
    └── words
        ├── points
        ├── transcription
        ├── language
        ├── illegibillity
        ├── orientation
        └── word_tags

Train.py

python train.py --data_dir {train data path} --val_data_dir {val data path} --name {wandb run name} --exp_name {model name
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