  This technical report describes a practical field test on word-image
classification in a very large collection of more than 300 diverse handwritten
historical manuscripts, with 1.6 million unique labeled images and more than 11
million images used in testing. Results indicate that several deep-learning
tests completely failed (mean accuracy 83%). In the tests with more than 1000
output units (lexical words) in one-hot encoding for classification,
performance steeply drops to almost zero percent accuracy, even with a modest
size of the pre-final (i.e., penultimate) layer (150 units). A traditional
feature method (BOVW) displays a consistent performance over numbers of classes
and numbers of training examples (mean accuracy 87%). Additional tests using
nearest mean on the output of the pre-final layer of an Inception V3 network,
for each book, only yielded mediocre results (mean accuracy 49\%), but was not
sensitive to high numbers of classes. Notably, this experiment was only
possible on the basis of labels that were harvested on the basis of a
traditional method which already works starting from a single labeled image per
class. It is expected that the performance of the failed deep learning tests
can be repaired, but only on the basis of human handcrafting (sic) of network
architecture and hyperparameters. When the failed problematic books are not
considered, end-to-end CNN training yields about 95% accuracy. This average is
dominated by a large subset of Chinese characters, performances for other
script styles being lower.
