Why pytesseract.image_to_data fails to recognize text?

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Hi! I am new to opencv,I am working on a project trying to recognize traffic signs.The enviroment I am going to use this project is indoors, it is for a self-driving small car which will have to navigate around a track. I've decided to first rescognize the shape of the object, then create a new picture from the ROI, and try to recognize the text on that picture.I am testing it with my laptop's webcamera which captures a 640x480 frame. The first issue i have is that picture that my code is trying to find the contours on is a little bit noisy. I tried several approaches, right now I am using median blurring--> Canny edge detection -->dialation. Is there something I am misssing? It provides relatively fair results on finding shapes, but it doesn't always work as inteded.(finds non positive results) Furthermore, the text recoginition part is also working but a fairly high fail rate. So it sometimes founds absolutely nonsense words, but it sometimes founds 'STOP'. I've also tought i could is pytesseract.image_to_string or image_to_boxes. Would any of that be a better approach? Here is my code:

import cv2
import pytesseract
import numpy as np
font = cv2.FONT_HERSHEY_COMPLEX
def empty(x):
        print(x)
        pass
#text recognition
def find_text(img):
    pytesseract.pytesseract.tesseract_cmd = 'C:\\Program Files (x86)\\Tesseract-OCR\\tesseract.exe'
    ##strings to find
    stop = "STOP"

    ############################

    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    img=cv2.resize(img,(300,150))

    ##word recognition
    hImg, wImg, _ = img.shape
    boxes = pytesseract.image_to_data(img)
    print(boxes)

    for x, b in enumerate(boxes.splitlines()):
        if x != 0:  ##first row headings
            b = b.split()  ##strings
            print(b)
            x1, y1, w1, h1 = int(b[6]), int(b[7]), int(b[8]), int(b[9])
            cv2.rectangle(img, (x1, y1), (w1 + x1, h1 + y1), (0, 0, 255), 3)
            cv2.imshow("roi", img)
            if len(b) == 12:
                if b[11] == stop:
                    print("found it")

#parameter window
cv2.namedWindow("parameters")
cv2.resizeWindow("paramaters",640,240)
#TRACKBARS 
cv2.createTrackbar("threshold1", "parameters", 81, 255, empty)
cv2.createTrackbar("threshold2", "parameters", 255, 255, empty)

kernel = np.ones((3, 3), np.uint8) ## maszk 
cap = cv2.VideoCapture(0)

while (cap.isOpened()):
    ret,frame   = cap.read()
    if ret == True:
        ##beolvasott video értékeinek felvétele
        width=cap.get(cv2.CAP_PROP_FRAME_WIDTH)
        height=cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
        fps=cap.get(cv2.CAP_PROP_FPS)
        imgcopy=frame ##másolat készítése

        #GET TRACKBAR POS
        threshold1 = cv2.getTrackbarPos('threshold1', "parameters")
        threshold2 = cv2.getTrackbarPos('threshold1', "parameters")

        grayvideo = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        blur =cv2.bilateralFilter(grayvideo,7,200,200)
        blur2 = cv2.medianBlur(grayvideo, 11)
        #bilblur=cv2.bilateralFilter(grayvideo,7,50,50)

        imgCanny = cv2.Canny(blur2, threshold1, threshold2)
        #ret3,th3 = cv2.threshold(grayvideo,threshold1,threshold2,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
        cv2.imshow("Canny", imgCanny)
        blur2 = cv2.medianBlur(imgCanny,1)
        cv2.imshow("blur2", blur2)

        dilated = cv2.dilate(imgCanny,kernel,iterations=1)
        cv2.imshow("dilated", dilated)


        contours,hierarchy =cv2.findContours(dilated,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)

            #print(len(contours))
            #area of conotur and boundig rect
        for contour in contours:  ##area of conotur and boundig rect ...