Trainining a custom model

decryptr allows you to train a custom keras model to identify the captcha contents. You will need to provide a human classified dataset with around 5000 captchas. Below we provide the code necessary to download a sample dataset and train a simple CNN model..

Downloading data

Before everything, let’s load the needed libraries in R.


You can download the data from our Google Storage using the R code below. It will contain around 13k classified captcha images that can be read with decryptr. The captcha comes from Receita Federal Brasileira and has the following format:


unzip("", exdir = "rfb")

Reading and preprocessing

decryptr has some usefull functions to read and transform the images to the data structure needed to train the models. The simplest way to transform the data is by running the code below:

captchas <- list.files("rfb/", full.names = TRUE, pattern = ".*png") %>%
  read_captcha(ans_in_path = TRUE, vocab = c(letters, 0:9)) %>%

We can then split our dataset into training and validation sets.

i <-$n, 10000)

train <- list(y = captchas$y[i,,,drop = FALSE], x = captchas$x[i,,,,drop = FALSE])
test <- list(y = captchas$y[-i,,,drop = FALSE], x = captchas$x[-i,,,,drop = FALSE])

Model specification in keras

Below we will specify a simple 3-layer CNN model using the keras sequential API. We will then compile the model defining the loss function and optimizers.

model <- keras_model_sequential()
model %>%
    input_shape = dim(train$x)[-1],
    filters = 16,
    kernel_size = c(5, 5),
    padding = "same",
    activation = "relu") %>%
  layer_max_pooling_2d() %>%
    filters = 32,
    kernel_size = c(5, 5),
    padding = "same",
    activation = "relu") %>%
  layer_max_pooling_2d() %>%
    filters = 64,
    kernel_size = c(5, 5),
    padding = "same",
    activation = "relu") %>%
  layer_max_pooling_2d() %>%
  layer_flatten() %>%
  layer_dense(units = 256) %>%
  layer_dropout(.1) %>%
  layer_dense(units = prod(dim(train$y)[-1])) %>%
  layer_reshape(target_shape = dim(train$y)[-1]) %>%

model %>%
    optimizer = "adam",
    loss = "categorical_crossentropy",
    metrics = "accuracy")

Model fitting

We can the fit the model by calling the fit function with the specified model and data (for training and validation).

model %>%
    x = train$x,
    y = train$y,
    batch_size = 32,
    epochs = 20,
    shuffle = TRUE,
    validation_data = list(test$x, test$y)

After training the model we should persist it to disk in order to be able to make predictions later. We can save it by calling:

save_model_hdf5(model, "rfb.hdf5")

When making predictions for a new captcha using a custom model it’s important to note the labs argument of the function load_model. It must be the same that you passed to the read_captcha when reading the captchas for training.

model <- load_model("rfb.hdf5", labs = c(letters, 0:9))
captcha <- read_captcha("path-to-captcha")
decrypt(captcha, model)

You can read more at Making predictions for new captchas.