  The text-independent approach to writer identification does not require the
writer to write some predetermined text. Previous research on text-independent
writer identification has been based on identifying writer-specific features
designed by experts. However, in the last decade, deep learning methods have
been successfully applied to learn features from data automatically. We propose
here an end-to-end deep-learning method for text-independent writer
identification that does not require prior identification of features. A
Convolutional Neural Network (CNN) is trained initially to extract local
features, which represent characteristics of individual handwriting in the
whole character images and their sub-regions. Randomly sampled tuples of images
from the training set are used to train the CNN and aggregate the extracted
local features of images from the tuples to form global features. For every
training epoch, the process of randomly sampling tuples is repeated, which is
equivalent to a large number of training patterns being prepared for training
the CNN for text-independent writer identification. We conducted experiments on
the JEITA-HP database of offline handwritten Japanese character patterns. With
200 characters, our method achieved an accuracy of 99.97% to classify 100
writers. Even when using 50 characters for 100 writers or 100 characters for
400 writers, our method achieved accuracy levels of 92.80% or 93.82%,
respectively. We conducted further experiments on the Firemaker and IAM
databases of offline handwritten English text. Using only one page per writer
to train, our method achieved over 91.81% accuracy to classify 900 writers.
Overall, we achieved a better performance than the previously published best
result based on handcrafted features and clustering algorithms, which
demonstrates the effectiveness of our method for handwritten English text also.
