Skip to content

Latest commit

 

History

History
63 lines (38 loc) · 2.35 KB

README.md

File metadata and controls

63 lines (38 loc) · 2.35 KB
Raw

dependencies repo count license last

Brezita

Brezita is a captcha solver and it can effectively predict the characters in the captcha image provided. Brezita is build open upon Tensorflow's CNN model deployed on Flask server.

Model Building

1. Dataset

We have used the dataset from kaggle here which contains 1070 captcha images. The images are 5 letter words that can contain numbers. The images have had noise applied to them (blur and a line).

2. Data preprocessing

We have used adaptive threshold to extract text from the images, dilated the image and added gaussian blur to image.

img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 175, 0)
img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, np.ones((4,2), np.uint8))
img = cv2.dilate(img, np.ones((2,2), np.uint8), iterations = 1)
img = cv2.GaussianBlur(img, (1,1), 0)

3. Training the model

We have used the keras models to train the model and were able to achieve 0.89 f1 score.

Checkout the colab here - https://colab.research.google.com/drive/17NlgFk1JjpBVhiVdvcSNofOJ4kVhhMj8?usp=sharing

Flask Application

Steps to setup Flask application.

  1. pipenv install This will create a virtualenv and install all the required packages.

  2. pipenv shell This will activate the virtualenv

  3. set FLASK_APP=main.py This will set main.py in the environment

  4. flask run This will start the flask server and you can visit the application on http://127.0.0.1:5000/

(OR)

If you are using Docker just use

  1. docker build -t brezita . to build the docker image

  2. docker run -p 5000:5000 brezita to spin the docker container and you can visit the application on http://127.0.0.1:5000/