A simple machine learning powered captcha breaker created using scikit-learn.For now the project is written inside a Jupyter notebook for a better visualization as this is just a proof of concept.
importmathimportosimportreimportnumpyasnpfromskimageimportimg_as_float,iofromskimage.colorimport*fromskimage.restorationimportdenoise_tv_chambollefromsklearnimportsvm,metrics# Files are named whatever-actualCaptchaTyped.pngconfirmed_images_re=re.compile(r'-([a-zA-Z0-9]{6})\.png$')frommatplotlibimportpyplotasplt%matplotlibinline
defload_image(path):img=img_as_float(rgb2gray(io.imread(path)))[9:38,10:177]img[img!=0]=1returnimgdefget_letters(img,number=6,avg_size=29):foriinrange(number):start=i*avg_sizenimg=img.copy()[:,start:start+avg_size]width_difference=avg_size-nimg.shape[1]ifwidth_difference!=0:nimg=np.append(nimg,np.ones((nimg.shape[0],width_difference)),axis=1)yieldnimgnot_trained_captcha=load_image('captchas/captcha-54f0d97919921-9ZAC1F.png')fig,ax=plt.subplots(ncols=6)fori,letterinenumerate(get_letters(not_trained_captcha)):ax[i].imshow(letter)
imgs= []limit_images=30000total_to_train=int(limit_images*0.8)loaded_images=0forfilenameinos.listdir('captchas'):match=confirmed_images_re.search(filename)ifnotmatch:continuetry:imgs.append((match.group(1).lower(),load_image('captchas/{}'.format(filename))))except (IndexError,OSError):# Pillow and its errorscontinueloaded_images+=1ifloaded_images==limit_images:breakprint('{} images'.format(len(imgs)))letters_image= []letters_ascii= []forimageinimgs:letters,image=imageforcolumn,letter_imageinenumerate(get_letters(image)):letters_image.append(letter_image.flatten())letters_ascii.append(letters[column])model=svm.SVC(C=10,gamma=0.001,probability=False)model.fit(letters_image[:total_to_train],letters_ascii[:total_to_train])
SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)
predicted=model.predict(letters_image[total_to_train:])expected=letters_ascii[total_to_train:]print(metrics.classification_report(expected,predicted))
precision recall f1-score support 0 0.66 0.75 0.70 1296 1 0.81 0.87 0.84 4128 2 0.94 0.95 0.95 4518 3 0.92 0.93 0.93 4420 4 0.96 0.98 0.97 4499 5 0.94 0.93 0.94 4271 6 0.90 0.94 0.92 4532 7 0.97 0.96 0.96 4578 8 0.87 0.90 0.88 4476 9 0.93 0.94 0.93 4593 a 0.98 0.97 0.97 4481 b 0.80 0.88 0.84 4339 c 0.93 0.89 0.91 4495 d 0.88 0.89 0.89 4548 e 0.90 0.90 0.90 4397 f 0.88 0.90 0.89 4359 g 0.89 0.90 0.90 4356 h 0.94 0.90 0.92 4371 i 0.81 0.83 0.82 4305 j 0.95 0.94 0.95 4363 k 0.93 0.92 0.92 4470 l 0.94 0.92 0.93 4267 m 0.97 0.95 0.96 4403 n 0.95 0.95 0.95 4501 o 0.84 0.78 0.81 4003 p 0.86 0.88 0.87 4457 q 0.95 0.96 0.96 4427 r 0.88 0.87 0.88 4399 s 0.95 0.89 0.92 4428 t 0.94 0.93 0.93 4537 u 0.95 0.90 0.92 4459 v 0.97 0.97 0.97 4412 w 0.97 0.96 0.96 4461 x 0.97 0.95 0.96 4508 y 0.97 0.94 0.95 4499 z 0.95 0.93 0.94 4444avg / total 0.92 0.92 0.92 156000
defdecode_captcha(filename,func=None):func=funcormodel.predictreturnfunc([l.flatten()forlinget_letters(load_image(filename))])filename='captchas/{}'.format(np.random.choice(os.listdir('captchas/')))print(filename,''.join(decode_captcha(filename,mo0del.predict)))captchas/captcha-54f0d89a91c67-OFUS8R.png 0fus8r
fromsklearn.grid_searchimportGridSearchCVparams= [{'kernel': ['rbf'],'gamma': [1e-3,1e-4],'C': [1,10,100,1000]}, {'kernel': ['linear'],'C': [1,10,100,1000]}]clf=GridSearchCV(svm.SVC(),params,n_jobs=1)# Use this to get the best params for the model# clf.fit(letters_image[:total_to_train], letters_ascii[:total_to_train])print(clf.best_estimator_)clf.grid_scores_
%timeitdecode_captcha('captchas/captcha-54f0d99253782-wh4ow7.png')10 loops, best of 3: 162 ms per loop
