如何提高EasyOCR的准确性/预测？

Question

我正在尝试从车辆牌照中获取字符。 但是很少有像这样的错误预测

我得到的输出是UP74 BD 3465，这是错误的。有许多例子将B预测为8等等

• 如何提高其准确性？
• 如何预处理图像以获得正确的预测或任何其他方法？

import matplotlib.pyplot as plt
import cv2
import easyocr
from pylab import rcParams
from IPython.display import Image

rcParams['figure.figsize'] = 8, 16
reader = easyocr.Reader(['en'])

output = reader.readtext(path)
for i in range(len(output)):
    print(output[i][-2])

Answer 1

• 首先，我建议您阅读关于OCR图像增强的主题：LINK

• 其次，在与上述主题相同的意义上，您可以在裁剪感兴趣区域（ROI）后，使用Thresholding、Gaussian Filtering和Histogram Equalization来解决此特定图像的问题，因此输出图像将如下所示：

输出将是：

UP14 BD 3465

import cv2
import easyocr
from pylab import rcParams
# import numpy library
import numpy as np

# define the path
path = 'input.png'

# read the image
img = cv2.imread(path, 0)

# find the white rectangle
th = img.copy()
th[th<200] = 0

bbox = np.where(th>0)
y0 = bbox[0].min()
y1 = bbox[0].max()
x0 = bbox[1].min()
x1 = bbox[1].max()

# crop the region of interest (ROI)
img = img[y0:y1, x0:x1]

# histogram equalization
equ = cv2.equalizeHist(img)
# Gaussian blur
blur = cv2.GaussianBlur(equ, (5, 5), 1)

# manual thresholding
th2 = 60 # this threshold might vary!
equ[equ>=th2] = 255
equ[equ<th2]  = 0

# Now apply the OCR on the processed image
rcParams['figure.figsize'] = 8, 16
reader = easyocr.Reader(['en'])

output = reader.readtext(equ)

for i in range(len(output)):
    print(output[i][-2])