  A text on an image often stores important information and directly carries
high level semantics, makes it as important source of information and become a
very active research topic. Many studies have shown that the use of CNN-based
neural networks is quite effective and accurate for image classification which
is the basis of text recognition. It can also be more enhanced by using
transfer learning from pre-trained model trained on ImageNet dataset as an
initial weight. In this research, the recognition is trained by using Chars74K
dataset and the best model results then tested on some samples of
IIIT-5K-Dataset. The research results showed that the best accuracy is the
model that trained using VGG-16 architecture applied with image transformation
of rotation 15{\deg}, image scale of 0.9, and the application of gaussian blur
effect. The research model has an accuracy of 97.94% for validation data,
98.16% for test data, and 95.62% for the test data from IIIT-5K-Dataset. Based
on these results, it can be concluded that pre-trained CNN can produce good
accuracy for text recognition, and the model architecture that used in this
study can be used as reference material in the development of text detection
systems in the future
