我有一堆日期我正在尝试使用tesseract进行OCR。 但是,日期中的许多数字与日期框中的行合并为:
此外,这是一个很好的形象,我可以很好地评价:
这是我的代码:
import os
import cv2
from matplotlib import pyplot as plt
import subprocess
import numpy as np
from PIL import Image
def show(img):
plt.figure(figsize=(20,20))
plt.imshow(img,cmap='gray')
plt.show()
def sort_contours(cnts, method="left-to-right"):
# initialize the reverse flag and sort index
reverse = False
i = 0
# handle if we need to sort in reverse
if method == "right-to-left" or method == "bottom-to-top":
reverse = True
# handle if we are sorting against the y-coordinate rather than
# the x-coordinate of the bounding box
if method == "top-to-bottom" or method == "bottom-to-top":
i = 1
# construct the list of bounding boxes and sort them from top to
# bottom
boundingBoxes = [cv2.boundingRect(c) for c in cnts]
cnts, boundingBoxes = zip(*sorted(zip(cnts, boundingBoxes),
key=lambda b:b[1][i], reverse=reverse))
# return the list of sorted contours and bounding boxes
return cnts, boundingBoxes
def tesseract_it(contours,main_img, label,delete_last_contour=False):
min_limit, max_limit = (1300,1700)
idx =0
roi_list = []
slist= set()
for cnt in contours:
idx += 1
x,y,w,h = cv2.boundingRect(cnt)
if label=='boxes':
roi=main_img[y+2:y+h-2,x+2:x+w-2]
else:
roi=main_img[y:y+h,x:x+w]
if w*h > min_limit and w*h < max_limit and w>10 and w< 50 and h>10 and h<50:
if (x,y,w,h) not in slist: # Stops from identifying repeted contours
roi = cv2.resize(roi,dsize=(45,45),fx=0 ,fy=0, interpolation = cv2.INTER_AREA)
roi_list.append(roi)
slist.add((x,y,w,h))
if not delete_last_contour:
vis = np.concatenate((roi_list),1)
else:
roi_list.pop(-1)
vis = np.concatenate((roi_list),1)
show(vis)
# Tesseract the final image here
# ...
image = 'bad_digit/1.jpg'
# image = 'bad_digit/good.jpg'
specimen_orig = cv2.imread(image,0)
specimen = cv2.fastNlMeansDenoising(specimen_orig)
# show(specimen)
kernel = np.ones((3,3), np.uint8)
# Now we erode
specimen = cv2.erode(specimen, kernel, iterations = 1)
# show(specimen)
_, specimen = cv2.threshold(specimen, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# show(specimen)
specimen_canny = cv2.Canny(specimen, 0, 0)
# show(specimen_canny)
specimen_blank_image = np.zeros((specimen.shape[0], specimen.shape[1], 3))
_,specimen_contours, retr = cv2.findContours(specimen_canny.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE )
# print(len(specimen_contours))
cv2.drawContours(specimen_blank_image, specimen_contours, -1, 100, 2)
# show(specimen_blank_image)
specimen_blank_image = np.zeros((specimen.shape[0], specimen.shape[1], 3))
specimen_sorted_contours, specimen_bounding_box = sort_contours(specimen_contours)
output_string = tesseract_it(specimen_sorted_contours,specimen_orig,label='boxes',)
# return output_string
附带的好图像的输出是这样的:
测试此图像确实给我准确的结果。
但是,对于那些行合并为数字的行,我的输出如下所示:
这些与Tesseract完全不兼容。 我想知道是否有办法删除线条,只保留数字。
我也尝试了以下内容: https://docs.opencv.org/3.2.0/d1/dee/tutorial_moprh_lines_detection.html
对于我附上的图片,这似乎并不是很好。
我也试过使用imagemagick:
convert original.jpg \
\( -clone 0 -threshold 50% -negate -statistic median 200x1 \) \
-compose lighten -composite \
\( -clone 0 -threshold 50% -negate -statistic median 1x200 \) \
-composite output.jpg
它的结果是公平的,但删除的行有点切断数字如下:
有没有更好的方法可以解决这个问题?我的最终目标是测试数字,因此最终图像确实需要非常清晰。
答案 0 :(得分:12)
以下是一些似乎运行良好的代码。有两个阶段:
以下是第一阶段后的一张图片的结果:
以下是第二阶段后的所有结果:
如你所见,它并不完美,8可以看作是B(好吧,就像我这样的人把它视为B ......但如果你的世界中只有数字,它就可以轻松修复)。还有一个&#34;:&#34;字符(来自已删除的垂直线的遗留物),我无法摆脱过多地调整代码...
C#代码:
static void Unbox(string inputFilePath, string outputFilePath)
{
using (var orig = new Mat(inputFilePath))
{
using (var gray = orig.CvtColor(ColorConversionCodes.BGR2GRAY))
{
using (var dst = orig.EmptyClone())
{
// this is what I call the "horizontal shake" pass.
// note I use the Rect shape here, this is important
using (var dilate = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(4, 1)))
{
Cv2.Dilate(gray, dst, dilate);
}
// erode just a bit to get back some numbers to life
using (var erode = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(2, 1)))
{
Cv2.Erode(dst, dst, erode);
}
// at this point, good OCR will see most numbers
// but we want to remove surrounding artifacts
// find countours
using (var canny = dst.Canny(0, 400))
{
var contours = canny.FindContoursAsArray(RetrievalModes.List, ContourApproximationModes.ApproxSimple);
// compute a bounding rect for all numbers w/o boxes and artifacts
// this is the tricky part where we try to discard what's not related exclusively to numbers
var boundingRect = Rect.Empty;
foreach (var contour in contours)
{
// discard some small and broken polygons
var polygon = Cv2.ApproxPolyDP(contour, 4, true);
if (polygon.Length < 3)
continue;
// we want only numbers, and boxes are approx 40px wide,
// so let's discard box-related polygons, if any
// and some other artifacts that passed previous checks
// this quite depends on some context knowledge...
var rect = Cv2.BoundingRect(polygon);
if (rect.Width > 40 || rect.Height < 15)
continue;
boundingRect = boundingRect.X == 0 ? rect : boundingRect.Union(rect);
}
using (var final = dst.Clone(boundingRect))
{
final.SaveImage(outputFilePath);
}
}
}
}
}
}
答案 1 :(得分:2)
答案 2 :(得分:0)
特别是,在Yves Daoust casus的情况下,请查看以下1
中的2018
...这几乎是"n"
或四分之三整数0
和8
成为字母B
。 2
可以被解读为6
。在某些情况下,0
也可以被视为6
等等。甚至有些可能最终会被视为&#34;无法识别&#34;如果你把网格留在原地。因此,我的方法是:
0,1, 2, 4, 5
和7
。一旦移除网格并完成训练,就可以轻松检测到某些数字的曲率。这会将90-95%的假阴性命中减少为真实整数(真阳性)或转向架(真阴性)。然后你只需担心那些5-10%。
可以找到文档和示例代码信息here at OpenCV,here at Code-Robin和here at github。
图片值02032018022018
: