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extract text from Driving Licence

Hi team, I could not able to remove image background(watermark) from Driving Licence.For this reason unable to extract text from image.Could anyone please help me on this.

Below is the sample image and code.

C:\fakepath\Driving Licence_1.PNG

import cv2 import numpy as np

src = cv2.imread("DL18.jpg") gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)

alpha = 3.0 beta = -200 new = alpha * gray + beta new = np.clip(new, 0, 255).astype(np.uint8)

blur = cv2.GaussianBlur(new, (3, 3), 0)

th1 = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)

dst = cv2.fastNlMeansDenoising(blur, None, 3, 7, 21)

cv2.imshow("OUTPUT", dst)

k = cv2.waitKey(0)

if k == 27: cv2.destroyAllWindows()

elif k == ord('s'): cv2.imwrite("/OUTPUT.jpg",dst)

cv2.destroyAllWindows()

extract text from Driving Licence

Hi team, I could not able to remove image background(watermark) from Driving Licence.For this reason unable to extract text from image.Could anyone please help me on this.

Below is the sample image and code.

C:\fakepath\Driving Licence_1.PNG

from imutils.object_detection import cv2 non_max_suppression import numpy as np

src = cv2.imread("DL18.jpg") gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)

alpha = 3.0 beta = -200 new = alpha * gray np import pytesseract pytesseract.pytesseract.tesseract_cmd = r'\Tesseract-OCR\tesseract.exe' import argparse import cv2

def decode_predictions(scores, geometry): (numRows, numCols) = scores.shape[2:4] rects = [] confidences = [] for y in range(0, numRows): scoresData = scores[0, 0, y] xData0 = geometry[0, 0, y] xData1 = geometry[0, 1, y] xData2 = geometry[0, 2, y] xData3 = geometry[0, 3, y] anglesData = geometry[0, 4, y]

    for x in range(0, numCols):

        if scoresData[x] < args["min_confidence"]:
        continue

        (offsetX, offsetY) = (x * 4.0, y * 4.0)

        angle = anglesData[x]
        cos = np.cos(angle)
    sin = np.sin(angle)

        h = xData0[x] + beta
new = np.clip(new, xData2[x]
        w = xData1[x] + xData3[x]

        endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x]))
        endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x]))
        startX = int(endX - w)
        startY = int(endY - h)

        rects.append((startX, startY, endX, endY))
        confidences.append(scoresData[x])

return (rects, confidences)

ap = argparse.ArgumentParser() ap.add_argument("-i", "--image", type=str, help="path to input image") ap.add_argument("-east", "--east", type=str, help="path to input EAST text detector") ap.add_argument("-c", "--min-confidence", type=float, default=0.5, help="minimum probability required to inspect a region") ap.add_argument("-w", "--width", type=int, default=320, help="nearest multiple of 32 for resized width") ap.add_argument("-e", "--height", type=int, default=320, help="nearest multiple of 32 for resized height") ap.add_argument("-p", "--padding", type=float, default=0.0, help="amount of padding to add to each border of ROI") args = vars(ap.parse_args())

image = cv2.imread(args["image"]) orig = image.copy() (origH, origW) = image.shape[:2]

(newW, newH) = (args["width"], args["height"]) rW = origW / float(newW) rH = origH / float(newH)

image = cv2.resize(image, (newW, newH)) (H, W) = image.shape[:2]

layerNames = [ "feature_fusion/Conv_7/Sigmoid", "feature_fusion/concat_3"]

print("[INFO] loading EAST text detector...") net = cv2.dnn.readNet(args["east"])

blob = cv2.dnn.blobFromImage(image, 1.0, (W, H), (400, 180, 20), swapRB=True, crop=False) net.setInput(blob) (scores, geometry) = net.forward(layerNames)

(rects, confidences) = decode_predictions(scores, geometry) boxes = non_max_suppression(np.array(rects), probs=confidences)

results = []

for (startX, startY, endX, endY) in boxes:

startX = int(startX * rW)
startY = int(startY * rH)
endX = int(endX * rW)
endY = int(endY * rH)

dX = int((endX - startX) * args["padding"])
dY = int((endY - startY) * args["padding"])

startX = max(0, startX - dX)
startY = max(0, startY - dY)
endX = min(origW, endX + (dX * 2))
endY = min(origH, endY + (dY * 2))


roi = orig[startY:endY, startX:endX]
crop_image = orig[startY:endY, startX:endX] 


config = ("-l eng --oem 1 --psm 7")
text = pytesseract.image_to_string(roi, config=config)


results.append(((startX, startY, endX, endY), text))

results = sorted(results, key=lambda r:r[0][1])

for ((startX, startY, endX, endY), text) in results:

print("OCR TEXT")
print("========")
print("{}\n".format(text))


text = "".join([c if ord(c) < 128 else "" for c in text]).strip()
output = orig.copy()
cv2.rectangle(output, (startX, startY), (endX, endY),
    (0, 0, 255).astype(np.uint8)

blur = cv2.GaussianBlur(new, (3, 3), 0)

th1 = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)

dst = cv2.fastNlMeansDenoising(blur, None, 3, 7, 21)

cv2.imshow("OUTPUT", dst)

k = cv2.waitKey(0)

if k == 27: cv2.destroyAllWindows()

elif k == ord('s'): cv2.imwrite("/OUTPUT.jpg",dst)

cv2.destroyAllWindows()

255), 2) cv2.putText(output, text, (startX, startY - 20), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 3) cv2.imshow("Text Detection", output) cv2.imwrite("output.jpg",output) cv2.waitKey(0)

extract text from Driving Licence

Hi team, I could not able to remove image background(watermark) from Driving Licence.For this reason unable to extract text from image.Could anyone please help me on this.

Below is the sample image and code.

C:\fakepath\Driving Licence_1.PNG

from imutils.object_detection import non_max_suppression import numpy as np import pytesseract pytesseract.pytesseract.tesseract_cmd = r'\Tesseract-OCR\tesseract.exe' import argparse import cv2

def decode_predictions(scores, geometry): geometry):

(numRows, numCols) = scores.shape[2:4]
 rects = []
 confidences = []
  for y in range(0, numRows):
     scoresData = scores[0, 0, y]
     xData0 = geometry[0, 0, y]
     xData1 = geometry[0, 1, y]
     xData2 = geometry[0, 2, y]
     xData3 = geometry[0, 3, y]
     anglesData = geometry[0, 4, y]

y]

    for x in range(0, numCols):

        if scoresData[x] < args["min_confidence"]:
args['min_confidence']:
            continue

        (offsetX, offsetY) = (x * 4.0, y * 4.0)

        angle = anglesData[x]
        cos = np.cos(angle)
     sin = np.sin(angle)

        h = xData0[x] + xData2[x]
        w = xData1[x] + xData3[x]

        endX = int(offsetX + (cos * xData1[x]) cos * xData1[x] + (sin * xData2[x]))
sin * xData2[x])
        endY = int(offsetY - (sin * xData1[x]) sin * xData1[x] + (cos * xData2[x]))
cos * xData2[x])
        startX = int(endX - w)
        startY = int(endY - h)

        rects.append((startX, startY, endX, endY))
        confidences.append(scoresData[x])

return (rects, confidences)

ap = argparse.ArgumentParser() ap.add_argument("-i", "--image", type=str, help="path ap.add_argument('-i', '--image', type=str, help='path to input image") ap.add_argument("-east", "--east", image') ap.add_argument('-east', '--east', type=str, help="path help='path to input EAST text detector") ap.add_argument("-c", "--min-confidence", detector') ap.add_argument('-c', '--min-confidence', type=float, default=0.5, help="minimum help='minimum probability required to inspect a region") ap.add_argument("-w", "--width", region') ap.add_argument('-w', '--width', type=int, default=320, help="nearest help='nearest multiple of 32 for resized width") ap.add_argument("-e", "--height", width') ap.add_argument('-e', '--height', type=int, default=320, help="nearest help='nearest multiple of 32 for resized height") ap.add_argument("-p", "--padding", height') ap.add_argument('-p', '--padding', type=float, default=0.0, help="amount help='amount of padding to add to each border of ROI") ROI') args = vars(ap.parse_args())

image = cv2.imread(args["image"]) cv2.imread(args['image']) orig = image.copy() (origH, origW) = image.shape[:2]

(newW, newH) = (args["width"], args["height"]) (args['width'], args['height']) rW = origW / float(newW) rH = origH / float(newH)

image = cv2.resize(image, (newW, newH)) (H, W) = image.shape[:2]

layerNames = [ "feature_fusion/Conv_7/Sigmoid", "feature_fusion/concat_3"]

print("[INFO] ['feature_fusion/Conv_7/Sigmoid', 'feature_fusion/concat_3' ]

print '[INFO] loading EAST text detector...") detector...' net = cv2.dnn.readNet(args["east"])cv2.dnn.readNet(args['east'])

blob = cv2.dnn.blobFromImage(image, 1.0, cv2.dnn.blobFromImage( image, 1.0, (W, H), (400, 180, 20), swapRB=True, crop=False) 20), swapRB=True, crop=False, ) net.setInput(blob) (scores, geometry) = net.forward(layerNames)

(rects, confidences) = decode_predictions(scores, geometry) boxes = non_max_suppression(np.array(rects), probs=confidences)

results = []

for (startX, startY, endX, endY) in boxes:

startX = int(startX * rW)
startY = int(startY * rH)
endX = int(endX * rW)
endY = int(endY * rH)

dX = int((endX - startX) * args["padding"])
args['padding'])
dY = int((endY - startY) * args["padding"])
args['padding'])

startX = max(0, startX - dX)
startY = max(0, startY - dY)
endX = min(origW, endX + (dX * 2))
dX * 2)
endY = min(origH, endY + (dY * 2))

dY * 2)

roi = orig[startY:endY, startX:endX]
crop_image = orig[startY:endY, startX:endX] 

startX:endX]

config = ("-l '-l eng --oem 1 --psm 7")
7'
text = pytesseract.image_to_string(roi, config=config)
 
results.append(((startX, startY, endX, endY), text))

results = sorted(results, key=lambda r:r[0][1])r: r[0][1])

for ((startX, startY, endX, endY), text) in results:

print("OCR TEXT")
print("========")
print("{}\n".format(text))

print 'OCR TEXT'
print '========'
print '{}\n'.format(text)

text = "".join([c ''.join([(c if ord(c) < 128 else "" '') for c in text]).strip()
output = orig.copy()
cv2.rectangle(output, (startX, startY), (endX, endY),
    endY), (0, 0, 255), 255),
              2)
cv2.putText(output, text, cv2.putText(
    output,
    text,
    (startX, startY - 20),
    cv2.FONT_HERSHEY_SIMPLEX, 1.2, cv2.FONT_HERSHEY_SIMPLEX,
    1.2,
    (0, 0, 255), 3)


cv2.imshow("Text Detection", 255),
    3,
    )

cv2.imshow('Text Detection', output)

cv2.imwrite("output.jpg",output)
cv2.imwrite('output.jpg', output)

cv2.waitKey(0)

extract text from Driving Licence

Hi team, I could not able to remove image background(watermark) from Driving Licence.For this reason unable to extract text from image.Could anyone please help me on this.

Below is the sample image and code.

C:\fakepath\Driving Licence_1.PNGC:\fakepath\Driving Licence_1.PNG

from imutils.object_detection import non_max_suppression import numpy as np import pytesseract pytesseract.pytesseract.tesseract_cmd = r'\Tesseract-OCR\tesseract.exe' import argparse import cv2

def decode_predictions(scores, geometry):

(numRows, numCols) = scores.shape[2:4]
rects = []
confidences = []

for y in range(0, numRows):

    scoresData = scores[0, 0, y]
    xData0 = geometry[0, 0, y]
    xData1 = geometry[0, 1, y]
    xData2 = geometry[0, 2, y]
    xData3 = geometry[0, 3, y]
    anglesData = geometry[0, 4, y]

    for x in range(0, numCols):

        if scoresData[x] < args['min_confidence']:
            continue

        (offsetX, offsetY) = (x * 4.0, y * 4.0)

        angle = anglesData[x]
        cos = np.cos(angle)
        sin = np.sin(angle)

        h = xData0[x] + xData2[x]
        w = xData1[x] + xData3[x]

        endX = int(offsetX + cos * xData1[x] + sin * xData2[x])
        endY = int(offsetY - sin * xData1[x] + cos * xData2[x])
        startX = int(endX - w)
        startY = int(endY - h)

        rects.append((startX, startY, endX, endY))
        confidences.append(scoresData[x])

return (rects, confidences)

ap = argparse.ArgumentParser() ap.add_argument('-i', '--image', type=str, help='path to input image') ap.add_argument('-east', '--east', type=str, help='path to input EAST text detector') ap.add_argument('-c', '--min-confidence', type=float, default=0.5, help='minimum probability required to inspect a region') ap.add_argument('-w', '--width', type=int, default=320, help='nearest multiple of 32 for resized width') ap.add_argument('-e', '--height', type=int, default=320, help='nearest multiple of 32 for resized height') ap.add_argument('-p', '--padding', type=float, default=0.0, help='amount of padding to add to each border of ROI') args = vars(ap.parse_args())

image = cv2.imread(args['image']) orig = image.copy() (origH, origW) = image.shape[:2]

(newW, newH) = (args['width'], args['height']) rW = origW / float(newW) rH = origH / float(newH)

image = cv2.resize(image, (newW, newH)) (H, W) = image.shape[:2]

layerNames = ['feature_fusion/Conv_7/Sigmoid', 'feature_fusion/concat_3' ]

print '[INFO] loading EAST text detector...' net = cv2.dnn.readNet(args['east'])

blob = cv2.dnn.blobFromImage( image, 1.0, (W, H), (400, 180, 20), swapRB=True, crop=False, ) net.setInput(blob) (scores, geometry) = net.forward(layerNames)

(rects, confidences) = decode_predictions(scores, geometry) boxes = non_max_suppression(np.array(rects), probs=confidences)

results = []

for (startX, startY, endX, endY) in boxes:

startX = int(startX * rW)
startY = int(startY * rH)
endX = int(endX * rW)
endY = int(endY * rH)

dX = int((endX - startX) * args['padding'])
dY = int((endY - startY) * args['padding'])

startX = max(0, startX - dX)
startY = max(0, startY - dY)
endX = min(origW, endX + dX * 2)
endY = min(origH, endY + dY * 2)

roi = orig[startY:endY, startX:endX]
crop_image = orig[startY:endY, startX:endX]

config = '-l eng --oem 1 --psm 7'
text = pytesseract.image_to_string(roi, config=config)

results.append(((startX, startY, endX, endY), text))

results = sorted(results, key=lambda r: r[0][1])

for ((startX, startY, endX, endY), text) in results:

print 'OCR TEXT'
print '========'
print '{}\n'.format(text)

text = ''.join([(c if ord(c) < 128 else '') for c in text]).strip()
output = orig.copy()
cv2.rectangle(output, (startX, startY), (endX, endY), (0, 0, 255),
              2)
cv2.putText(
    output,
    text,
    (startX, startY - 20),
    cv2.FONT_HERSHEY_SIMPLEX,
    1.2,
    (0, 0, 255),
    3,
    )

cv2.imshow('Text Detection', output)

cv2.imwrite('output.jpg', output)

cv2.waitKey(0)

extract text from Driving Licence

Hi team, I could not able to remove image background(watermark) from Driving Licence.For this reason unable to extract text from image.Could anyone please help me on this.

C:\fakepath\Driving Licence_1.PNG

Below is the sample image and code.

C:\fakepath\Driving Licence_1.PNG

from imutils.object_detection import non_max_suppression import numpy as np import pytesseract pytesseract.pytesseract.tesseract_cmd = r'\Tesseract-OCR\tesseract.exe' import argparse import cv2

def decode_predictions(scores, geometry):

(numRows, numCols) = scores.shape[2:4]
rects = []
confidences = []

for y in range(0, numRows):

    scoresData = scores[0, 0, y]
    xData0 = geometry[0, 0, y]
    xData1 = geometry[0, 1, y]
    xData2 = geometry[0, 2, y]
    xData3 = geometry[0, 3, y]
    anglesData = geometry[0, 4, y]

    for x in range(0, numCols):

        if scoresData[x] < args['min_confidence']:
            continue

        (offsetX, offsetY) = (x * 4.0, y * 4.0)

        angle = anglesData[x]
        cos = np.cos(angle)
        sin = np.sin(angle)

        h = xData0[x] + xData2[x]
        w = xData1[x] + xData3[x]

        endX = int(offsetX + cos * xData1[x] + sin * xData2[x])
        endY = int(offsetY - sin * xData1[x] + cos * xData2[x])
        startX = int(endX - w)
        startY = int(endY - h)

        rects.append((startX, startY, endX, endY))
        confidences.append(scoresData[x])

return (rects, confidences)

ap = argparse.ArgumentParser() ap.add_argument('-i', '--image', type=str, help='path to input image') ap.add_argument('-east', '--east', type=str, help='path to input EAST text detector') ap.add_argument('-c', '--min-confidence', type=float, default=0.5, help='minimum probability required to inspect a region') ap.add_argument('-w', '--width', type=int, default=320, help='nearest multiple of 32 for resized width') ap.add_argument('-e', '--height', type=int, default=320, help='nearest multiple of 32 for resized height') ap.add_argument('-p', '--padding', type=float, default=0.0, help='amount of padding to add to each border of ROI') args = vars(ap.parse_args())

image = cv2.imread(args['image']) orig = image.copy() (origH, origW) = image.shape[:2]

(newW, newH) = (args['width'], args['height']) rW = origW / float(newW) rH = origH / float(newH)

image = cv2.resize(image, (newW, newH)) (H, W) = image.shape[:2]

layerNames = ['feature_fusion/Conv_7/Sigmoid', 'feature_fusion/concat_3' ]

print '[INFO] loading EAST text detector...' net = cv2.dnn.readNet(args['east'])

blob = cv2.dnn.blobFromImage( image, 1.0, (W, H), (400, 180, 20), swapRB=True, crop=False, ) net.setInput(blob) (scores, geometry) = net.forward(layerNames)

(rects, confidences) = decode_predictions(scores, geometry) boxes = non_max_suppression(np.array(rects), probs=confidences)

results = []

for (startX, startY, endX, endY) in boxes:

startX = int(startX * rW)
startY = int(startY * rH)
endX = int(endX * rW)
endY = int(endY * rH)

dX = int((endX - startX) * args['padding'])
dY = int((endY - startY) * args['padding'])

startX = max(0, startX - dX)
startY = max(0, startY - dY)
endX = min(origW, endX + dX * 2)
endY = min(origH, endY + dY * 2)

roi = orig[startY:endY, startX:endX]
crop_image = orig[startY:endY, startX:endX]

config = '-l eng --oem 1 --psm 7'
text = pytesseract.image_to_string(roi, config=config)

results.append(((startX, startY, endX, endY), text))

results = sorted(results, key=lambda r: r[0][1])

for ((startX, startY, endX, endY), text) in results:

print 'OCR TEXT'
print '========'
print '{}\n'.format(text)

text = ''.join([(c if ord(c) < 128 else '') for c in text]).strip()
output = orig.copy()
cv2.rectangle(output, (startX, startY), (endX, endY), (0, 0, 255),
              2)
cv2.putText(
    output,
    text,
    (startX, startY - 20),
    cv2.FONT_HERSHEY_SIMPLEX,
    1.2,
    (0, 0, 255),
    3,
    )

cv2.imshow('Text Detection', output)

cv2.imwrite('output.jpg', output)

cv2.waitKey(0)

extract text from Driving Licence

Hi team, I could not able to remove image background(watermark) from Driving Licence.For this reason unable to extract text from image.Could anyone please help me on this.

C:\fakepath\Driving Licence_1.PNG

Below is the sample image and code.code:

from imutils.object_detection import non_max_suppression
import numpy as np
import pytesseract
pytesseract.pytesseract.tesseract_cmd = r'\Tesseract-OCR\tesseract.exe'
import argparse
import cv2

cv2 def decode_predictions(scores, geometry):

geometry):

    (numRows, numCols) = scores.shape[2:4]
 rects = []
 confidences = []

 for y in range(0, numRows):

     scoresData = scores[0, 0, y]
     xData0 = geometry[0, 0, y]
     xData1 = geometry[0, 1, y]
     xData2 = geometry[0, 2, y]
     xData3 = geometry[0, 3, y]
     anglesData = geometry[0, 4, y]

     for x in range(0, numCols):

         if scoresData[x] < args['min_confidence']:
             continue

         (offsetX, offsetY) = (x * 4.0, y * 4.0)

         angle = anglesData[x]
         cos = np.cos(angle)
         sin = np.sin(angle)

         h = xData0[x] + xData2[x]
         w = xData1[x] + xData3[x]

         endX = int(offsetX + cos * xData1[x] + sin * xData2[x])
         endY = int(offsetY - sin * xData1[x] + cos * xData2[x])
         startX = int(endX - w)
         startY = int(endY - h)

         rects.append((startX, startY, endX, endY))
         confidences.append(scoresData[x])

 return (rects, confidences)

ap = argparse.ArgumentParser() ap.add_argument('-i', '--image', type=str, help='path to input image') ap.add_argument('-east', '--east', type=str, help='path to input EAST text detector') ap.add_argument('-c', '--min-confidence', type=float, default=0.5, help='minimum probability required to inspect a region') ap.add_argument('-w', '--width', type=int, default=320, help='nearest multiple of 32 for resized width') ap.add_argument('-e', '--height', type=int, default=320, help='nearest multiple of 32 for resized height') ap.add_argument('-p', '--padding', type=float, default=0.0, help='amount of padding to add to each border of ROI') args = vars(ap.parse_args())

vars(ap.parse_args()) image = cv2.imread(args['image']) orig = image.copy() (origH, origW) = image.shape[:2]

image.shape[:2] (newW, newH) = (args['width'], args['height']) rW = origW / float(newW) rH = origH / float(newH)

float(newH) image = cv2.resize(image, (newW, newH)) (H, W) = image.shape[:2]

image.shape[:2] layerNames = ['feature_fusion/Conv_7/Sigmoid', 'feature_fusion/concat_3' ]

] print '[INFO] loading EAST text detector...' net = cv2.dnn.readNet(args['east'])

cv2.dnn.readNet(args['east']) blob = cv2.dnn.blobFromImage( image, 1.0, (W, H), (400, 180, 20), swapRB=True, crop=False, ) net.setInput(blob) (scores, geometry) = net.forward(layerNames)

net.forward(layerNames) (rects, confidences) = decode_predictions(scores, geometry) boxes = non_max_suppression(np.array(rects), probs=confidences)

probs=confidences) results = []

[] for (startX, startY, endX, endY) in boxes:

boxes:

    startX = int(startX * rW)
 startY = int(startY * rH)
 endX = int(endX * rW)
 endY = int(endY * rH)

 dX = int((endX - startX) * args['padding'])
 dY = int((endY - startY) * args['padding'])

 startX = max(0, startX - dX)
 startY = max(0, startY - dY)
 endX = min(origW, endX + dX * 2)
 endY = min(origH, endY + dY * 2)

 roi = orig[startY:endY, startX:endX]
 crop_image = orig[startY:endY, startX:endX]

 config = '-l eng --oem 1 --psm 7'
 text = pytesseract.image_to_string(roi, config=config)

 results.append(((startX, startY, endX, endY), text))

results = sorted(results, key=lambda r: r[0][1])

r[0][1]) for ((startX, startY, endX, endY), text) in results:

results:

    print 'OCR TEXT'
 print '========'
 print '{}\n'.format(text)

 text = ''.join([(c if ord(c) < 128 else '') for c in text]).strip()
 output = orig.copy()
 cv2.rectangle(output, (startX, startY), (endX, endY), (0, 0, 255),
               2)
 cv2.putText(
     output,
     text,
     (startX, startY - 20),
     cv2.FONT_HERSHEY_SIMPLEX,
     1.2,
     (0, 0, 255),
     3,
     )

 cv2.imshow('Text Detection', output)

 cv2.imwrite('output.jpg', output)

 cv2.waitKey(0)