Pytesseract, OCR 정확도 향상

크기를 1.2에서 2로 변경하고 모든 전처리를 제거했습니다. psm 11 및 psm 12로 좋은 결과를 얻었습니다.

import pytesseract
import cv2
import numpy as np

img = cv2.imread('wavy.png')

#  img = cv2.resize(img, None, fx=1.2, fy=1.2, interpolation=cv2.INTER_CUBIC)
img = cv2.resize(img, None, fx=2, fy=2)

img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

kernel = np.ones((1,1), np.uint8)
#  img = cv2.dilate(img, kernel, iterations=1)
#  img = cv2.erode(img, kernel, iterations=1)

#  img = cv2.threshold(cv2.medianBlur(img, 3), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

cv2.imwrite('thresh.png', img)

pytesseract.pytesseract.tesseract_cmd = 'C:\\Program Files (x86)\\Tesseract-OCR\\tesseract.exe'
    
for psm in range(6,13+1):
    config = '--oem 3 --psm %d' % psm
    txt = pytesseract.image_to_string(img, config = config, lang='eng')
    print('psm ', psm, ':',txt)

이 config = '--oem 3 --psm %d' % psm줄은 문자열 보간 (%) 연산자 를 %d사용하여 정수 (psm)로 바꿉니다. 정확히 무엇을하는지 모르겠지만 oem사용하는 습관이 생겼습니다. psm이 답변의 끝에서 더 자세히 알아보십시오 .

psm  11 : those he large form might light another us should name

took mountain story important went own own thought girl

over family look some much ask the under why miss point

make mile grow do own school was

psm  12 : those he large form might light another us should name

took mountain story important went own own thought girl

over family look some much ask the under why miss point

make mile grow do own school was

psm페이지 분할 모드의 약자입니다. 다른 모드가 무엇인지 정확히 모르겠습니다. 설명에서 코드가 무엇인지 알 수 있습니다. 목록을 가져올 수 있습니다.tesseract --help-psm

Page segmentation modes:
  0    Orientation and script detection (OSD) only.
  1    Automatic page segmentation with OSD.
  2    Automatic page segmentation, but no OSD, or OCR. (not implemented)
  3    Fully automatic page segmentation, but no OSD. (Default)
  4    Assume a single column of text of variable sizes.
  5    Assume a single uniform block of vertically aligned text.
  6    Assume a single uniform block of text.
  7    Treat the image as a single text line.
  8    Treat the image as a single word.
  9    Treat the image as a single word in a circle.
 10    Treat the image as a single character.
 11    Sparse text. Find as much text as possible in no particular order.
 12    Sparse text with OSD.
 13    Raw line. Treat the image as a single text line,
       bypassing hacks that are Tesseract-specific.