6 분 소요

안녕하세요, 이번 포스팅에서는 국내 OCR 의 “전통적 강자” Naver Clova 의 OCR API를 사용하여 테스트 이미지를 인식해보겠습니다. OCR 테스를 위해서는 먼저 Naver 에 회원 가입이 되어있어야 합니다. Clova Console에 접속하여 OCR API 사용을 위한 준비를 마치고, Colab과 같은 jupyter notebook 환경에서 테스트를 진행해 보고자 합니다. 8가지 이상의 여러 OCR 서비스들을 테스트 해보았을 때, Clova의 OCR 서비스는 신청 및 사용 절차가 상대적으로 간편하고 documentation이 친절하게 되어 있는 편이라 처음 사용하시는 분들이 시도하기에 좋은 API라고 생각됩니다. 일반 OCR 모델 뿐만 아니라 영수증, 사업자등록증, 명함과 같은 특정 서류를 인식하는 특화 모델(specialized models)들도 다양하게 제공하고 있는 것으로 보아, Clova내부에서도 지속적으로 학습/투자하고 있는 서비스가 아닐까 생각됩니다.

0. Naver Clova OCR API 신청 하기


API호출을 위해서는 먼저 Domain 에서 도메인을 생성해야 합니다. 네이버에서 제공하는 공식 문서를 참고하여 순서대로 진행해보도록 하겠습니다.

0-1.회원가입

Naver Clova 홈페이지에 접속해서,Services 탭을 누르면 CLOVA OCR 서비스를 찾을 수 있습니다.

dashboard

Services 탭을 누른 후 AI Services 밑에 있는 CLOVA OCR 클릭합니다.

services

0-2. 콘솔 도메인 이동

CLOVA OCR의 Domain으로 이동하면 도메인 생성 -> 일반 템플릿 을 눌러 모델을 생성합니다.

console

0-3. 도메인 생성

아래 내용을 참고하여 기입하여 도메인을 생성합니다.

credit

요금: 기능 및 요금 안내 페이지에서는 각 서비스별 이용에 대한 요금이 나옵니다. 일반 OCR 모델로 사진 한두장을 테스트하실 정도라면 비용 발생은 걱정하지 않으셔도 됩니다. 단, 고급 플랜을 사용하거나 특화모델의 경우 도메인 생성 및 유지 비용이 나올 수 있으니 유의하셔서 사용하시길 바랍니다.

0-4. API Getaway 연동

생성된 도메인이 도메인 페이지에 추가될 겁니다. 도메인 페이지의 옵션 에서 API Gateway 연동을 클릭합니다.

api_getaway

생성을 눌러 Secret Key를 생성합니다. Secret Key와 API Invoke URL 을 확인할 수 있습니다. 두 개의 키를 가지고 추후에 jupyter notebook 에서 API 호출을 할 겁니다.

secret_key

자 이제 API 사용을 위한 모든 준비가 끝났습니다! 이제 코랩이나 jupyter notebook 환경으로 가볼게요.

1. 네이버 클로바 OCR API 사용 하기


아래는 이미지 1건에 대한 Upstage OCR 인식 결과, 5건의 multiple 이미지에 대한 결과, 그리고 이미지별 소요시간을 출력한 상세 코드 입니다.

5건의 이미지와 서비스별, 이미지별 소요시간 비교 그래프는 OCR 비교평가 포스팅에 별도로 정리해놓았으니, 서비스별 비교평가가 필요한 독자 분들은 해당 포스팅에서 확인할 수 있습니다.

1-0. 라이브러리 임포트

import time
import pandas as pd
import cv2
import json
import matplotlib.pyplot as plt

import shutil
import os
import random
try:
 from PIL import Image
except ImportError:
 import Image

import requests
import uuid
import time
import json

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 5000)
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive

1-1. Basic Usage

  • json 형식으로 결과가 출력된다
secret_key = YOUR_SECRET_KEY
api_url = API_INVOKE_URL
image_file = '/content/drive/MyDrive/ocr_test_images/한글_pdf_리포트_표.png'

request_json = {
    'images': [
        {
            'format': 'jpg',
            'name': 'demo'
        }
    ],
    'requestId': str(uuid.uuid4()),
    'version': 'V2',
    'timestamp': int(round(time.time() * 1000))
}

payload = {'message': json.dumps(request_json).encode('UTF-8')}
files = [
  ('file', open(image_file,'rb'))
]
headers = {
  'X-OCR-SECRET': secret_key
}

response = requests.request("POST", api_url, headers=headers, data = payload, files = files)

print(response.text.encode('utf8'))
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1-2. 개별 이미지에 대한 결과 확인 하기

  • OpenCV cv2 라이브러리를 활용하여 원본 이미지 에서 OCR이 인식된 영역을 bounding box로 표시해볼 수 있습니다.
# Setting up the request JSON
request_json = {
    'images': [
        {
            'format': 'jpg',
            'name': 'demo'
        }
    ],
    'requestId': str(uuid.uuid4()),
    'version': 'V2',
    'timestamp': int(round(time.time() * 1000))
}

payload = {'message': json.dumps(request_json).encode('UTF-8')}
files = [('file', open(image_file,'rb'))]
headers = {'X-OCR-SECRET': secret_key}

# Make the OCR request
response = requests.request("POST", api_url, headers=headers, data=payload, files=files)

# Load the original image for visualization
image = cv2.imread(image_file)
highlighted_image = image.copy()

# OCR 응답 처리
if response.status_code == 200:
    ocr_results = json.loads(response.text)
    all_texts = []  # 모든 텍스트를 저장할 리스트
    for image_result in ocr_results['images']:
        for field in image_result['fields']:
            text = field['inferText']
            all_texts.append(text)  # 텍스트 추가

            # 텍스트 주변에 빨간 사각형 그리기
            bounding_box = field['boundingPoly']['vertices']
            start_point = (int(bounding_box[0]['x']), int(bounding_box[0]['y']))
            end_point = (int(bounding_box[2]['x']), int(bounding_box[2]['y']))
            cv2.rectangle(highlighted_image, start_point, end_point, (0, 0, 255), 2)

    # 모든 텍스트를 띄어쓰기로 연결하여 출력
    full_text = ' '.join(all_texts)
    print(full_text)
else:
    print(f"OCR 결과를 받아오지 못했습니다. 상태 코드: {response.status_code}")


# Display the original and highlighted images side by side
fig, axs = plt.subplots(1, 2, figsize=(15, 10))
axs[0].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
axs[0].set_title('Original Image')
axs[0].axis('off')

axs[1].imshow(cv2.cvtColor(highlighted_image, cv2.COLOR_BGR2RGB))
axs[1].set_title('Highlighted Image')
axs[1].axis('off')

plt.show()
OCR Result:
SPRi 이슈리포트 IS-160 생성AI의 부상과 산업의 영향 1. 논의 배경 1.1 생성AI의 부상 2022년은 주요 빅테크 기업들의 생성AI가 본격적으로 공개된 한해 o 생성 AI(Generative AI)는 입력된 데이터를 통해 사용자가 원하는 결과를 유추해 텍스트, 오디오, 비디오 형태의 결과물을 만들어 내는 AI 알고리즘 - 2022년 한해 동안 언어, 음성, 비디오 등에서 주요한 생성 모델이 등장 * (언어) DeepMind Chinchilla(7B, '22.3). Google PALM(540B, '22.4). Meta의 OPT-1758('22.5). 허깅페이스의 BLOOM (176B, '22.7). ChatGPT(175B, '22.11), OpenAl GPT-4('23.3), Meta의 LLAMA (7B~65B, '23.2) (8는 십억개 파라미터) (이미지) Meta의 Make-A-Scenee('22.3) OpenAI의 DALL-E2 공개('22.4), Google의 Imagen(22.5) 스태빌리티AI의 Stable Diffusion ('22.8) 미드저니랩의 Midiourney ('22.7) (오디오·비디오) Meta의 Make-A-Video('22.9). Google MusicLM(오디오. 23.1). 런웨이 Gen-1(영상, '23.2). Gen-2(영상, '23.3) OpenAI는는 ChatGPT 출시로 생성AI 시대의 본격적인 도래를 알림 0 OpenAI의 ChatGPT 등장 이후 기존 빅테크기업과 스타트업에서 초거대 언어모델(LLM) 중심의 AI생성 모델 경쟁 본격화 - 마이크로소프트(ChatSPT)와 구글(Bard)이 시장을 주도하고 있는 가운데 메타, 아마존, 앤트로픽AI 등 미국 기업들과 BAT(바이두, 알리바바, 텐센트)를 포함한 중국 AI기업들도 대응 서비스 출시 [표 1-1] ChatGPT 이후의 생성AI 모델 출시 현황 (예시) 사례 AI모델 서비스 비고 . ChatGPT(GPT-3.5)를 GPT-4로 확장하면서 데이터 최신성, ChatGPT(175B) 신뢰성(환각증상 완화), 멀티모달 지원 기능 강화 마이크로소프트 미국 GPT-4 ° Bing검색엔진에 통합 Edge브라우저에 이용 MS-Office 365, Teams 등 업무생산성 도구 결합 예고 구글의 ChatGPT 대항마 BARD 서비스 공개 시연('23.2) 구글 BARD 서비스 미국, 영국에서 출시('23.3) 구글 LAMDA (137B) 미국 구글 AI 개발 조직인 딥마인드와 브레인 전격 통합 (23.4) 구글 1/0 행사에서 개선된 BARD 180개국 전면출시('23.5) LLAMA 모델 공개('23.2)를 통해 오픈소스 생태계 활성화 LLAMA (7B, 13B, 메타 스탠포드 대학에서 LLAMA을 파인튜닝한 경량화 AI모델 미국 33B, 65B) Alpaca개발 (23.3) 1 SPRi 소프트웨어정책연구소

single_image

1-3. 5가지 테스트 이미지 적용

  • 저의 테스트 초기 목적은 8가지 OCR 서비스에 대한 API 호출 및 사용 난이도/복잡도 파악과 서비스별 소요 시간 이었습니다. 이번에는 인식 결과 텍스트와 테스트 이미지별로 소요시간까지 함께 추출해 보겠습니다. 추후에 인식된 텍스트는 원문 텍스트와 함께 오차 등을 비교하며 정량적인 방법으로도 성능 평가가 가능할 것 같네요. 하지만, 이번 포스팅에서는 dataframe 화하여 추출하는 것까지 진행해보겠습니다.

ocr_result_df에 각 이미지별 인식 결과 텍스트와 소요시간이 담겼습니다

# 테스트 이미지 파일 경로 목록
image_paths = {
    '한국어 영수증': '/content/drive/MyDrive/ocr_test_images/한글_영수증.png',
    '영어 영수증': '/content/drive/MyDrive/ocr_test_images/whole-foods-market.jpg',
    '영어 PDF 표': '/content/drive/MyDrive/ocr_test_images/영문_PDF_표.png',
    '한국어 PDF 표': '/content/drive/MyDrive/ocr_test_images/한글_pdf_리포트_표.png',
    '영어 I-20 문서': '/content/drive/MyDrive/ocr_test_images/영문_Form_I-20.png'
}

# 결과를 저장할 DataFrame
ocr_results_df = pd.DataFrame(columns=['이미지 유형', 'OCR 결과', '처리 시간 (초)'])

# 각 이미지를 처리하고 결과 저장
for image_type, image_path in image_paths.items():
    start_time = time.time()

    # 요청 데이터 준비
    request_json = {
        'images': [{'format': 'jpg', 'name': 'demo'}],
        'requestId': str(uuid.uuid4()),
        'version': 'V2',
        'timestamp': int(round(time.time() * 1000))
    }
    payload = {'message': json.dumps(request_json).encode('UTF-8')}
    files = [('file', open(image_path, 'rb'))]
    headers = {'X-OCR-SECRET': secret_key}

    # 요청 전송
    response = requests.request("POST", api_url, headers=headers, data=payload, files=files)
    processing_time = time.time() - start_time

    # 응답 처리
    if response.status_code == 200:
        ocr_result = json.loads(response.text)
        text_results = " ".join([field['inferText'] for image in ocr_result['images'] for field in image['fields']])
    else:
        text_results = "처리 실패"

    ocr_results_df = ocr_results_df.append({
        '이미지 유형': image_type,
        'OCR 결과': text_results,
        '처리 시간 (초)': processing_time
    }, ignore_index=True)

# DataFrame 출력
print(ocr_results_df)

아래와 같이 5개의 이미지에 대한 인식 결과 텍스트와 소요시간이 담긴 ocr_results_df가 생성되었습니다.

result

2. 사용 후기


8가지 서비스를 비교 평가해본, 서비스 이용자 관점에서의 저의 주관적인 사용 후기는 아래와 같습니다.

  • 인식률: 한글, 영문 모두 무난하고, 한영 모두, 글자 수에 상관없이 준수한 속도를 보여주고 있다.
  • 특화 모델의 다양성으로는 국내 1등이라고 생각된다. 현재 영수증, 사업자등록증, 명함, 신용카드, 신분증 등 다양한 특화모델을 여러 가지 플랜으로 제공하는 것으로 알고 있는데, 내부적으로 OCR 서비스를 지속적으로 투자하는 것으로 보이기 때문에 기대하고 지켜볼 만한 것 같다.
  • 친절도 측면: 타 서비스에 비해 Documentation 이 굉장히 자세하게 작성되어 있는 편이라 처음 Clova 서비스를 사용하는 유저에게도 진입 장벽이 낮을 것으로 생각된다. UI/UX 또한 직관적인 편. OCR 모델 종류와 플랜이 워낙 다양하다 보니, 나의 목적에 맞는 모델과 플랜을 “잘” 고르는 것이 조금 헷갈릴 수 있을 것 같다. 도메인 비용 또한 모델 및 플랜별로 상이하게 책정되므로, 고객센터에 직접 문의 하거나 첫 한 달간은 요금 산정되는 것을 잘 지켜봐야 한다. 도메인 띄워놓고 까먹고 한 달 후에 엥? 라는 금액이 나올수도..

3. 유의 사항


  • Clova OCR 의 billing system 은 모델과 플랜에 따라 책정 금액이 상이합니다. 따라서, domain 을 생성하고 삭제할 필요 없는 경우도 있으나, 도메인이 유지되는 것만으로 비용이 발생하는 경우도 있기 때문에, 안전하게 하기 위해서는 도메인을 항상 삭제하는 습관을 들이는 게 좋아보입니다.

  • 사용량(Usage)은 Console 내 결제 정보 메뉴에서 조회가 가능하나, 실시간 이용량 조회가 지원되지 않기 때문에, 단시간 내에 여러 건의 테스트를 진행한다면, 대략적으로 본인이 몇 건을 호출하는지 감을 잡고 진행하는 것이 좋을 것 같다는 생각이 드네요.

  • 더 자세한 정보는 공식 Documentation 를 참고해주세요.

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