python - Recognize the characters of license plate

Question

I try to recognize the characters of license plates using OCR, but my licence plate have worse quality.

I'm trying to somehow improve character recognition for OCR, but my best result is this:result.

And even tesseract on this picture does not recognize any character. My code is:

#include <cv.h>         // open cv general include file
#include <highgui.h>    // open cv GUI include file
#include <iostream>     // standard C++ I/O
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <string>

using namespace cv;

int main( int argc, char** argv )
{
    Mat src;
    Mat dst;

    Mat const structure_elem = getStructuringElement(
                         MORPH_RECT, Size(2,2));

    src = imread(argv[1], CV_LOAD_IMAGE_COLOR);   // Read the file

    cvtColor(src,src,CV_BGR2GRAY);
    imshow( "plate", src );

    GaussianBlur(src, src, Size(1,1), 1.5, 1.5);
    imshow( "blur", src );

    equalizeHist(src, src);
    imshow( "equalize", src );

    adaptiveThreshold(src, src, 255, ADAPTIVE_THRESH_GAUSSIAN_C, CV_THRESH_BINARY, 15, -1);
    imshow( "threshold", src );

    morphologyEx(src, src, MORPH_CLOSE, structure_elem);
    imshow( "morphological operation", src );

    imwrite("end.jpg", src);

    waitKey(0);
    return 0;
}

And my question is, do you know how to achieve better results? More clear image? Despite having my licence plate worse quality, so that the result could read OCR (for example Tesseract).

Thank you for answers. Really I do not know how to do it.

Answer 1

One possible algorithm to clean up the images is as follows:

• Scale the image up, so that the letters are more substantial.
• Reduce the image to only 8 colours by k-means clustering.
• Threshold the image, and erode it to fill in any small gaps and make the letters more substantial.
• Invert the image to make masking easier.
• Create a blank mask image of the same size, set to all zeros
• Find contours in the image. For each contour:
  • Find bounding box of the contour
  • Find the area of the bounding box
  • If the area is too small or too large, drop the contour (I chose 1000 and 10000 as limits)
  • Otherwise draw a filled rectangle corresponding to the bounding box on the mask with white colour (255)
  • Store the bounding box and the corresponding image ROI
• For each separated character (bounding box + image)
  • Recognise the character

---

Note: I prototyped this in Python 2.7 with OpenCV 3.1. C++ ports of this code are near the end of this answer.

---

Character Recognition

I took inspiration for the character recognition from this question on SO.

Then I found an image that we can use to extract training images for the correct font. I cut them down to only include digits and letters without accents.

train_digits.png:

train_letters.png:

Then i wrote a script that splits the individual characters, scales them up and prepares the training images that contain single character per file:

import os
import cv2
import numpy as np

# ============================================================================    

def extract_chars(img):
    bw_image = cv2.bitwise_not(img)
    contours = cv2.findContours(bw_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[1]

    char_mask = np.zeros_like(img)
    bounding_boxes = []
    for contour in contours:
        x,y,w,h = cv2.boundingRect(contour)
        x,y,w,h = x-2, y-2, w+4, h+4
        bounding_boxes.append((x,y,w,h))


    characters = []
    for bbox in bounding_boxes:
        x,y,w,h = bbox
        char_image = img[y:y+h,x:x+w]
        characters.append(char_image)

    return characters

# ============================================================================    

def output_chars(chars, labels):
    for i, char in enumerate(chars):
        filename = "chars/%s.png" % labels[i]
        char = cv2.resize(char
            , None
            , fx=3
            , fy=3
            , interpolation=cv2.INTER_CUBIC)
        cv2.imwrite(filename, char)

# ============================================================================    

if not os.path.exists("chars"):
    os.makedirs("chars")

img_digits = cv2.imread("train_digits.png", 0)
img_letters = cv2.imread("train_letters.png", 0)

digits = extract_chars(img_digits)
letters = extract_chars(img_letters)

DIGITS = [0, 9, 8 ,7, 6, 5, 4, 3, 2, 1]
LETTERS = [chr(ord('A') + i) for i in range(25,-1,-1)]

output_chars(digits, DIGITS)
output_chars(letters, LETTERS)

# ============================================================================ 

---

The next step was to generate the training data from the character files we created with the previous script.

I followed the algorithm from the answer to the question mentioned above, resizing each character image to 10x10 and using all the pixels as keypoints.

I save the training data as char_samples.data and char_responses.data

Script to generate training data:

import cv2
import numpy as np

CHARS = [chr(ord('0') + i) for i in range(10)] + [chr(ord('A') + i) for i in range(26)]

# ============================================================================

def load_char_images():
    characters = {}
    for char in CHARS:
        char_img = cv2.imread("chars/%s.png" % char, 0)
        characters[char] = char_img
    return characters

# ============================================================================

characters = load_char_images()

samples =  np.empty((0,100))
for char in CHARS:
    char_img = characters[char]
    small_char = cv2.resize(char_img,(10,10))
    sample = small_char.reshape((1,100))
    samples = np.append(samples,sample,0)

responses = np.array([ord(c) for c in CHARS],np.float32)
responses = responses.reshape((responses.size,1))

np.savetxt('char_samples.data',samples)
np.savetxt('char_responses.data',responses)

# ============================================================================

---

Once we have the training data created, we can run the main script:

import cv2
import numpy as np

# ============================================================================

def reduce_colors(img, n):
    Z = img.reshape((-1,3))

    # convert to np.float32
    Z = np.float32(Z)

    # define criteria, number of clusters(K) and apply kmeans()
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
    K = n
    ret,label,center=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)

    # Now convert back into uint8, and make original image
    center = np.uint8(center)
    res = center[label.flatten()]
    res2 = res.reshape((img.shape))

    return res2 

# ============================================================================

def clean_image(img):
    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    resized_img = cv2.resize(gray_img
        , None
        , fx=5.0
        , fy=5.0
        , interpolation=cv2.INTER_CUBIC)

    resized_img = cv2.GaussianBlur(resized_img,(5,5),0)
    cv2.imwrite('licence_plate_large.png', resized_img)

    equalized_img = cv2.equalizeHist(resized_img)
    cv2.imwrite('licence_plate_equ.png', equalized_img)


    reduced = cv2.cvtColor(reduce_colors(cv2.cvtColor(equalized_img, cv2.COLOR_GRAY2BGR), 8), cv2.COLOR_BGR2GRAY)
    cv2.imwrite('licence_plate_red.png', reduced)


    ret, mask = cv2.threshold(reduced, 64, 255, cv2.THRESH_BINARY)
    cv2.imwrite('licence_plate_mask.png', mask) 

    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    mask = cv2.erode(mask, kernel, iterations = 1)
    cv2.imwrite('licence_plate_mask2.png', mask)

    return mask

# ============================================================================

def extract_characters(img):
    bw_image = cv2.bitwise_not(img)
    contours = cv2.findContours(bw_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[1]

    char_mask = np.zeros_like(img)
    bounding_boxes = []
    for contour in contours:
        x,y,w,h = cv2.boundingRect(contour)
        area = w * h
        center = (x + w/2, y + h/2)
        if (area > 1000) and (area < 10000):
            x,y,w,h = x-4, y-4, w+8, h+8
            bounding_boxes.append((center, (x,y,w,h)))
            cv2.rectangle(char_mask,(x,y),(x+w,y+h),255,-1)

    cv2.imwrite('licence_plate_mask3.png', char_mask)

    clean = cv2.bitwise_not(cv2.bitwise_and(char_mask, char_mask, mask = bw_image))

    bounding_boxes = sorted(bounding_boxes, key=lambda item: item[0][0])  

    characters = []
    for center, bbox in bounding_boxes:
        x,y,w,h = bbox
        char_image = clean[y:y+h,x:x+w]
        characters.append((bbox, char_image))

    return clean, characters


def highlight_characters(img, chars):
    output_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
    for bbox, char_img in chars:
        x,y,w,h = bbox
        cv2.rectangle(output_img,(x,y),(x+w,y+h),255,1)

    return output_img

# ============================================================================    

img = cv2.imread("licence_plate.jpg")

img = clean_image(img)
clean_img, chars = extract_characters(img)

output_img = highlight_characters(clean_img, chars)
cv2.imwrite('licence_plate_out.png', output_img)


samples = np.loadtxt('char_samples.data',np.float32)
responses = np.loadtxt('char_responses.data',np.float32)
responses = responses.reshape((responses.size,1))


model = cv2.ml.KNearest_create()
model.train(samples, cv2.ml.ROW_SAMPLE, responses)

plate_chars = ""
for bbox, char_img in chars:
    small_img = cv2.resize(char_img,(10,10))
    small_img = small_img.reshape((1,100))
    small_img = np.float32(small_img)
    retval, results, neigh_resp, dists = model.findNearest(small_img, k = 1)
    plate_chars += str(chr((results[0][0])))

print("Licence plate: %s" % plate_chars)

---

Script Output

Enlarged 5x:

Equalized:

Reduced to 8 colours:

Thresholded:

Eroded:

Mask selecting only characters:

Clean image with bounding boxes:

Console output:

Licence plate: 2B99996

---

C++ code, using OpenCV 2.4.11 and Boost.Filesystem to iterate over files in a directory.

#include <boost/filesystem.hpp>

#include <opencv2/opencv.hpp>

#include <iostream>
#include <string>
// ============================================================================
namespace fs = boost::filesystem;
// ============================================================================
typedef std::vector<std::string> string_list;
struct char_match_t
{
    cv::Point2i position;
    cv::Mat image;
};
typedef std::vector<char_match_t> char_match_list;
// ----------------------------------------------------------------------------
string_list find_input_files(std::string const& dir)
{
    string_list result;

    fs::path dir_path(dir);

    fs::directory_iterator end_itr;
    for (fs::directory_iterator i(dir_path); i != end_itr; ++i) {
        if (!fs::is_regular_file(i->status())) continue;

        if (i->path().extension() == ".png") {
            result.push_back(i->path().string());
        }        
    }
    return result;
}
// ----------------------------------------------------------------------------
cv::Mat reduce_image(cv::Mat const& img, int K)
{
    int n = img.rows * img.cols;
    cv::Mat data = img.reshape(1, n)