bilibili-captcha

Overview

The Python program attempts to recognize CAPTCHA images generated by bilibili.com.

The program is developed and tested on Mac OS X 10.10.4 and Windows 10 using Python 3.4 interpreter. There might be problems if the program is run under other environments. Particularly, the program will not compile using Python 2 because of the differences in syntax.

Dependencies

Usage

Clone all the files and put them under the same directory as your program, including the dataset folder.

Key Files Overview

captcha_provider.py

This module represents the source of the CAPTCHA. An abstract base class HttpCaptchaProvider is defined to represent abstract CAPTCHA providers and a derived class BilibiliCaptchaProvider is defined to represent the specific CAPTCHA source one is attempting to recognize. If you want to define another CAPTCHA source, you can define another class that inherits the abstract base class. Two methods that must be overridden are _get_data_from_seq and _is_correct_response. _get_data_from_seq extracts the necessary data that need to be submitted as part of the request to verify. _is_correct_response checks whether the recognized character sequence is correct.

captcha_recognizer.py

This module is designed to recognize the images (the return value of imread function in matplotlib.image) from a particular CAPTCHA source (derived class of HttpCaptchaProvider). The steps to recognize an image are

Noise reduction

1.1 Noise reduction by hsv (remove_noise_with_hsv function)

1.2 Noise reduction by neighbors (remove_noise_with_neighbors function)

After this step the remaining image is a greyscale image with slight noise.
Segmentation

Components are found using the next-nearest-neighbor. (segment_with_label and other commands)

After this step the image is partitioned into a number of "characters".
Recognition

If the number of partitioned characters is as desired, each is recognized using the model in captcha_learn.py. (recognize function)

captcha_learn.py

This module is designed to use a multilayer perceptron (MLP) model to learn to recognize individual CAPTCHA character. The input layer consists of 300 neurons, which is the result of flattening the standard 20 by 15 CAPTCHA character image. There is only one hidden layer, and it consists of 200 hidden neurons. The output layer consists of 26 neurons, each corresponding to one possible outcome (EFGH JKLMN PQR TUVWXY 123456 89). The activation function of hidden units is tanh. The activation function of output units is the softmax function. The model is trained by using stochastic gradient descent with minibatches. There is already a model with tuned parameters, and it is saved in best_model.pkl. If you want to reconstruct the model, call reconstruct_model method. If you want to make prediction, call predict method.

Example

Let's take a look at a few examples.

Suppose we want to recognize a bilibili CAPTCHA image called img (the return value of imread in matplotlib.image), we can

import time

import config as c
import dataset_manager
from captcha_recognizer import CaptchaRecognizer
from captcha_provider import BilibiliCaptchaProvider
from helper import show_image, time_func
import captcha_learn

provider = BilibiliCaptchaProvider()
recognizer = CaptchaRecognizer()
success, seq, weak_confidence = recognizer.recognize(img, save_intermediate=True, verbose=True,reconstruct=False, force_partition=False)

For the parameters, save_intermediate controls whether intermediate files should be saved. If set to true, those files will be created under directory /temp. verbose controls whether additional information like timing is printed out. reconstruct controls whether reconstructing the learning model is needed. force_partition controls whether the recognizer recognizes those images that are partitioned into four pieces. This parameter is designed to be used in cases under which we want to maximize our chance of success when we cannot overlook the number of times the partition fails. However, in real world situations, it is usually the case that we can regenerate CAPTCHA as many times as we want, so we do not have to worry about the number of times the partition fails.

For the return values, success is a boolean variable indicating whether the recognition is successful. seq is the recognized sequence if the recognition is successful. weak_confidence is a boolean variable indicating whether we are only weakly confident about the partition. It is not relevant here because the variable force_partition is set to be false.

Now suppose we want to test whether a CAPTCHA fetched from bilibili.com is correct for n times, we can

import time

import config as c
import dataset_manager
from captcha_recognizer import CaptchaRecognizer
from captcha_provider import BilibiliCaptchaProvider
from helper import show_image, time_func
import captcha_learn

test_recognize_http(num=n)

For an explanation of output, see section "Benchmark" below.

Benchmark

In a benchmark test on an Amazon EC2 instance, 1000 CAPTCHA images were fetched from bilibili.com (test_recognize_http(num=1000)). The recognition result is as follows:

Fail:  233
Right weak:  78
Right strong:  412
Right total:  490
Wrong weak:  260
Wrong strong:  17
Wrong total:  277
Total success rate:  0.49
Success rate when confident:  0.6388526727509778
Success rate when strongly confident:  0.9603729603729604
Success rate when weakly confident:  0.23076923076923078
Time used to test recognize http is:  350.77492213249207

According to the result, 490 out of 1000 CAPTCHAs were recognized successfully, and therefore the total success rate is about 49%. We also notice that when the model is strongly confident of the result, i.e. when the image can be successfully partitioned into 5 characters, the success rate is as high as 96%. Therefore, in practical situations where the total success rate may not be as important because normally one could ask the server to regenerate the CAPTCHA if not strongly confident, the success rate would be nearly 100%.

Deep Learning Tutorial

Image manipulation and processing using Numpy and Scipy

Multilayer Perceptron

Scikit-image: image processing