SVM always gives the same prediction

Question

I have a page and I am trying to predict letters on the page by using SVM. However, the prediction result always the same. As a result, I am unable to read the page. This is my code to detect the letters from the line images(I separated lines before):

def letters(img):
rect_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
morph_letters = cv2.morphologyEx(img, cv2.MORPH_CROSS, rect_kernel) 
cv2.imshow("morph", morph_letters)
cnts = cv2.findContours(morph_letters.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnts = imutils.grab_contours(cnts)
lengthOfContours = len(cnts)
print("Number of counturs: ", lengthOfContours)
points = []
rects = []

for (i, c) in enumerate(cnts):
    area = cv2.contourArea(c)
    if area > 80:
        print("area  "+str(area))
        (x, y, w, h) = cv2.boundingRect(c)
        x = x - 3
        y = y - 10
        w = w + 4
        h = h + 12
        cv2.rectangle(img_letters, (x, y), (x + w, y + h), (0, 255, 0), 2)
        rect = [x, y, w+x, h+y]
        leftPoint = x
        #print("leftPoint: ", leftPoint)
        points.append(leftPoint)
        points.sort()
        rects.append([i,rect])
        rects.sort(key=lambda x: x[1])
        #print("rects: ", rects)
        if i == lengthOfContours-1:
        
            for i in range(len(rects)):
                
                found_letter = thresh_adaptive[rects[i][1][1]:rects[i][1][3], rects[i][1][0]:rects[i][1][2]]
                prediction(found_letter)

And this is my prediction code. It takes the letter image from def letters function, name: found_letter. However, the result is always the same, l.

def prediction(word_img):
pick = open('model-letters.sav', 'rb')
#pickle.dump(model, pick)
model = pickle.load(pick)
pick.close()

#prediction = model.predict(x_test)
#accuracy = model.score(x_test, y_test)

categories = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z','A','B','C','D','E','F','G','H','I','J',  'K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z','1','2','3','4',  '5','6','7','8','9','0']

img = word_img
try:
    img = cv2.resize(img, (50,50))
    img = np.array(img).flatten()
    img = img.reshape(1, -1)
    print("working")
    prediction = model.predict(img)
    letter = categories[prediction[0]]
    print("Prediction ", prediction)
    print("Prediction letter: ", letter)
    #print("Accuracy: ",accuracy)
    letterImg = img
    cv2.imshow("letter", letterImg)
    cv2.waitKey(0)

    fh = open("letters.txt", "a")
    fh.write(letter)
    fh.close()

except:
    print("error")

And this is my for loop to gives all the line images to def letters(img) function.

index = -1
for i in range(30,-1,-1):
    index = index + 1
    dir = "images-lines2/line" + str(i) + ".png"
    img_orj = cv2.imread(dir)
    img_orj = cv2.resize(img_orj, None, fx=1, fy=1.5, interpolation=cv2.INTER_AREA)
    img_letters = img_orj.copy()
    gray = cv2.cvtColor(img_orj, cv2.COLOR_BGR2GRAY)
    img_lines = img_orj.copy()
    blur_gaus = cv2.GaussianBlur(gray,(3,3),0)
    kernel = np.ones((3,3), np.uint8)
    thresh_adaptive = cv2.adaptiveThreshold(blur_gaus,255,cv2.  ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV,7,11)

    letter_img = letters(thresh_adaptive)

If you want to see cv2.imshow("letter", letterImg), this is the output

Any help is appreciated.

SVM always gives the same prediction

0 Answers