Text Recognize using tesseract - python-3.x

Question

Hello I'm trying to recognize text from Image using Tesseract but unable to get result.
I'm using EAST technique to detect text. I've one more question how can I extend padding of the box. cv2.putText does not work in this case.
original code for text detection: https://github.com/opencv/opencv/blob/master/samples/dnn/text_detection.cpp
import cv2
import numpy as np
import argparse
import time
import math
import matplotlib.pyplot as plt
import skimage.io as io
import os
from imutils.object_detection import non_max_suppression
import pytesseract
print(np.__version__)
def decode_predictions(scores, geometry):
**# grab the number of rows and columns from the scores volume, then
# initialize our set of bounding box rectangles and corresponding
# confidence scores**
(numRows, numCols) = scores.shape[2:4]
boxes = []
confidences = []
**# loop over the number of rows**
for y in range(0, numRows):
**# extract the scores (probabilities), followed by the geometrical
# data used to derive potential bounding box coordinates that
# surround text**
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]
**# loop over the number of columns**
for x in range(0, numCols):
**# if our score does not have sufficient probability, ignore it**
if scoresData[x] < args["min_confidence"]:
continue
**# compute the offset factor as our resulting feature maps will
# be 4x smaller than the input image**
(offsetX, offsetY) = (x * 4.0, y * 4.0)
**# extract the rotation angle for the prediction and then
# compute the sin and cosine**
angle = anglesData[x]
cos = np.cos(angle)
sin = np.sin(angle)
**# use the geometry volume to derive the width and height of
# the bounding box**
h = xData0[x] + xData2[x]
w = xData1[x] + xData3[x]
**# compute the rotated rect for
# the text prediction bounding box**
offset = (offsetX + (cos * xData1[x]) + (sin * xData2[x]), offsetY - (sin * xData1[x]) + (cos * xData2[x]))
p1 = (-sin * h + offset[0], -cos * h + offset[1])
p3 = (-cos * w + offset[0], sin * w + offset[1])
center = (0.5*(p1[0]+p3[0]), 0.5*(p1[1]+p3[1]))
**# add the bounding box coordinates and probability score to
# our respective lists**
boxes.append((center, (w,h), -angle * 180.0 / math.pi))
confidences.append(float(scoresData[x]))
return (boxes, confidences)
args = {
"image":"C:\\Users\\ckunwar\\Test_Images\\licence_plate1\\52.jpg",
"east": "frozen_east_text_detection.pb",
"min_confidence":0.25,
"nms_thresh": 0.24,
"width":480,
"height":320,
"padding":0.0
}
**# load the input image and grab the image dimensions**
image = cv2.imread(args["image"])
orig = image.copy()
(H, W) = image.shape[:2]
#print(H,W)
**# set the new width and height and then determine the ratio in change
# for both the width and height**
(newW, newH) = (args["width"], args["height"])
rW = W / float(newW)
rH = H / float(newH)
**# resize the image and grab the new image dimensions**
image = cv2.resize(image, (newW, newH))
(H, W) = image.shape[:2]
**# define the two output layer names for the EAST detector model that
# we are interested -- the first is the output probabilities and the
# second can be used to derive the bounding box coordinates of text**
layerNames = ["feature_fusion/Conv_7/Sigmoid","feature_fusion/concat_3"]
**# load the pre-trained EAST text detector**
print("[INFO] loading EAST text detector...")
net = cv2.dnn.readNet(args["east"])
**# construct a blob from the image and then perform a forward pass of
# the model to obtain the two output layer sets**
blob = cv2.dnn.blobFromImage(image, 1.0, (W, H),(123.68, 116.78, 103.94), swapRB=True, crop=False)
start = time.time()
net.setInput(blob)
(scores, geometry) = net.forward(layerNames)
end = time.time()
**# show timing information on text prediction**
print("[INFO] text detection took {:.6f} seconds".format(end - start))
(boxes, confidences) = decode_predictions(scores, geometry)
**# apply non-maxima suppression to suppress weak, overlapping bounding boxes**
indices = cv2.dnn.NMSBoxesRotated(boxes, confidences, args["min_confidence"], args["nms_thresh"])
results = []
**# loop over the bounding boxes**
for i in indices:
**# get 4 corners of the rotated rect**
vertices = cv2.boxPoints(boxes[i[0]])
**# scale the bounding box coordinates based on the respective ratios**
for j in [0,1,2,3]:
vertices[j][0] *= rW
vertices[j][1] *= rH
**# draw the bounding box on the image**
for j in [0,1,2,3]:
p1 = (vertices[j][0], vertices[j][1])
p2 = (vertices[(j + 1) % 4][0], vertices[(j + 1) % 4][1])
config = ("-l eng --oem 3 --psm 11")
text = pytesseract.image_to_string(orig,config=config)
results.append(((p1,p2), text))
results = sorted(results, key=lambda r:r[0][1])
output = orig.copy()
for ((p1,p2), text) in results:
print("OCR TEXT")
print("========")
print("{}\n".format(text))
text = "".join([c if ord(c) < 128 else "" for c in text]).strip()
cv2.line(output, p1, p2, (0, 255, 0), 2)
#cv2.rectangle(output, p1, p2,(0, 255, 0), 2)
cv2.putText(output, text,cv2.FONT_HERSHEY_TRIPLEX, 0.8, (0, 0, 255), 2)
**# show the output image**
#orig = cv2.cvtColor(orig, cv2.COLOR_BGR2RGB)
cv2.imshow("Text Detection", output)
cv2.waitKey(0)