Captcha Solving Using TensorFlow

Introduction

Solve captcha using TensorFlow.
Learn CNN and TensorFlow by a practical project.

Follow the steps, run the code, and it works!

the accuracy of 4 digits version can be as high as 99.8%!

There are several more steps to put this prototype on production.

Ping me for paid technical supports.

i@jackon.me

Solve Captcha Using CNN Model
- Training: 4-digits Captcha
- Training: 4-letters Captcha
- Inference: load trained model and predict given images
Generate DataSet for Training
- Usage
- Example 1: 4 chars per captcha, use digits only
- Example 2: sampling random images

Solve Captcha Using CNN Model

old code that using tensorflow 1.x is moved to tensorflow_v1.

Training: 4-digits Captcha

this is a perfect project for beginers.

we will train a model of ~90% accuracy in 1 minute using one single GPU card (GTX 1080 or above).

if we increase the dataset by 10x, the accuracy increases to 98.8%. we can further increase the accuracy to 99.8% using 1M traning images.

here is the source code and running logs: captcha-solver-tf2-4digits-AlexNet-98.8.ipynb

Images, Ground Truth and Predicted Values:

there is 1 predicton error out of the 20 examples below. 9871 -> 9821

Accuracy and Loss History:

Model Structure:

3 convolutional layers, followed by 2x2 max pooling layer each.
1 flatten layer
2 dense layer

Training: 4-letters Captcha

this is a more practical project.

the code is the same as the 4-digits version, but the training dataset is much bigger.

it costs 2-3 hours to generate training dataset and costs 30 min to train a 95% accuracy model.

here is the source code and running logs: captcha-solver-tf2-4letters-AlexNet.ipynb

Inference: load trained model and predict given images

example: captcha-solver-model-restore.ipynb

Generate DataSet for Training

Usage

$ python datasets/gen_captcha.py  -h
usage: gen_captcha.py [-h] [-n N] [-c C] [-t T] [-d] [-l] [-u] [--npi NPI] [--data_dir DATA_DIR]

optional arguments:
  -h, --help           show this help message and exit
  -n N                 epoch number of character permutations.
  -c C                 max count of images to generate. default unlimited
  -t T                 ratio of test dataset.
  -d, --digit          use digits in dataset.
  -l, --lower          use lowercase in dataset.
  -u, --upper          use uppercase in dataset.
  --npi NPI            number of characters per image.
  --data_dir DATA_DIR  where data will be saved.

examples:

Example 1: 4 chars per captcha, use digits only

1 epoch has 10*9*8*7=5040 images, generate 6 epoches for training.

generating the dataset:

$ python datasets/gen_captcha.py -d --npi=4 -n 6
10 choices: 0123456789
generating 6 epoches of captchas in ./images/char-4-epoch-6/train
generating 1 epoches of captchas in ./images/char-4-epoch-6/test
write meta info in ./images/char-4-epoch-6/meta.json

preview the dataset:

$ python datasets/base.py images/char-4-epoch-6/
========== Meta Info ==========
num_per_image: 4
label_choices: 0123456789
height: 100
width: 120
n_epoch: 6
label_size: 10
==============================
train images: (30240, 100, 120), labels: (30240, 40)
test images: (5040, 100, 120), labels: (5040, 40)

Example 2: sampling random images

scenario: use digits/upper cases, 4 chars per captcha image.

1 epoch will have 36*35*34*33=1.4M images. the dataset is too big to debug.

using -c 10000 param, sampling 10k random images.