Real-time OCR with openCV EAST & Tesseract.
Requires openCV 3.4.2 or above.
Requires Tesseract 4.0 or above.
For text recognition on a live web-cam feed:
python real_time_ocr.py --east frozen_east_text_detection.pb
For text recognition on a video file:
python real_time_ocr.py --east frozen_east_text_detection.pb --video test.avi
For text detection on image:
python text_detection.py --image test.png --east frozen_east_text_detection.pb
For text detection on video or web-cam:
python video_text_detection.py --east frozen_east_text_detection.pb
python video_text_detection.py --east frozen_east_text_detection.pb --video test.avi
For text recognition on image:
python text_recognition.py --east frozen_east_text_detection.pb --image test.png
python text_recognition.py --east frozen_east_text_detection.pb --image test.png --padding 0.25
from imutils.video import VideoStream
from imutils.video import FPS
from imutils.object_detection import non_max_suppression
import numpy as np
import argparse
import imutils
import time
import cv2
import pytesseract
# setting up tesseract path
pytesseract.pytesseract.tesseract_cmd = r'/usr/local/Cellar/tesseract/5.0.1/bin/tesseract'
def box_extractor(scores, geometry, min_confidence):
num_rows, num_cols = scores.shape[2:4]
rectangles = []
confidences = []
for y in range(num_rows):
scores_data = scores[0, 0, y]
x_data0 = geometry[0, 0, y]
x_data1 = geometry[0, 1, y]
x_data2 = geometry[0, 2, y]
x_data3 = geometry[0, 3, y]
angles_data = geometry[0, 4, y]
for x in range(num_cols):
if scores_data[x] < min_confidence:
continue
offset_x, offset_y = x * 4.0, y * 4.0
angle = angles_data[x]
cos = np.cos(angle)
sin = np.sin(angle)
box_h = x_data0[x] + x_data2[x]
box_w = x_data1[x] + x_data3[x]
end_x = int(offset_x + (cos * x_data1[x]) + (sin * x_data2[x]))
end_y = int(offset_y + (cos * x_data2[x]) - (sin * x_data1[x]))
start_x = int(end_x - box_w)
start_y = int(end_y - box_h)
rectangles.append((start_x, start_y, end_x, end_y))
confidences.append(scores_data[x])
return rectangles, confidences
def get_arguments():
ap = argparse.ArgumentParser()
ap.add_argument('-v', '--video', type=str,
help='path to optional video file')
ap.add_argument('-east', '--east', type=str, required=True,
help='path to EAST text detection model')
ap.add_argument('-c', '--min_confidence', type=float, default=0.5,
help='minimum confidence to process a region')
ap.add_argument('-w', '--width', type=int, default=320,
help='resized image width (multiple of 32)')
ap.add_argument('-e', '--height', type=int, default=320,
help='resized image height (multiple of 32)')
ap.add_argument('-p', '--padding', type=float, default=0.0,
help='padding on each ROI border')
arguments = vars(ap.parse_args())
return arguments
if __name__ == '__main__':
args = get_arguments()
w, h = None, None
new_w, new_h = args['width'], args['height']
ratio_w, ratio_h = None, None
layer_names = ['feature_fusion/Conv_7/Sigmoid', 'feature_fusion/concat_3']
print("[INFO] loading EAST text detector...")
net = cv2.dnn.readNet(args["east"])
if not args.get('video', False):
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(1)
else:
vs = cv2.VideoCapture(args['video'])
fps = FPS().start()
while True:
frame = vs.read()
frame = frame[1] if args.get('video', False) else frame
if frame is None:
break
frame = imutils.resize(frame, width=1000)
orig = frame.copy()
orig_h, orig_w = orig.shape[:2]
if w is None or h is None:
h, w = frame.shape[:2]
ratio_w = w / float(new_w)
ratio_h = h / float(new_h)
frame = cv2.resize(frame, (new_w, new_h))
blob = cv2.dnn.blobFromImage(frame, 1.0, (new_w, new_h), (123.68, 116.78, 103.94),
swapRB=True, crop=False)
net.setInput(blob)
scores, geometry = net.forward(layer_names)
rectangles, confidences = box_extractor(scores, geometry, min_confidence=args['min_confidence'])
boxes = non_max_suppression(np.array(rectangles), probs=confidences)
for (start_x, start_y, end_x, end_y) in boxes:
start_x = int(start_x * ratio_w)
start_y = int(start_y * ratio_h)
end_x = int(end_x * ratio_w)
end_y = int(end_y * ratio_h)
dx = int((end_x - start_x) * args['padding'])
dy = int((end_y - start_y) * args['padding'])
start_x = max(0, start_x - dx)
start_y = max(0, start_y - dy)
end_x = min(orig_w, end_x + (dx * 2))
end_y = min(orig_h, end_y + (dy * 2))
# ROI to be recognized
roi = orig[start_y:end_y, start_x:end_x]
# recognizing text
config = '-l eng --oem 1 --psm 7'
text = pytesseract.image_to_string(roi, config=config)
cv2.rectangle(orig, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2)
cv2.putText(orig, text, (start_x, start_y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 3)
fps.update()
cv2.imshow("Detection", orig)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
fps.stop()
print(f"[INFO] elapsed time {round(fps.elapsed(), 2)}")
print(f"[INFO] approx. FPS : {round(fps.fps(), 2)}")
if not args.get('video', False):
vs.stop()
else:
vs.release()
cv2.destroyAllWindows()#RUN THIS TO SEE THE DETECTION
!python real_time_ocr.py --east frozen_east_text_detection.pb --video test.avi#RESULTS: Screenshots
from IPython.display import Image
Image("Screenshot.png")
