  In this work, we adhere to explore a Multi-Tasking learning (MTL) based
network to perform document attribute classification such as the font type,
font size, font emphasis and scanning resolution classification of a document
image. To accomplish these tasks, we operate on either segmented word level or
on uniformed size patches randomly cropped out of the document. Furthermore, a
hybrid convolution neural network (CNN) architecture "MTL+MI", which is based
on the combination of MTL and Multi-Instance (MI) of patch and word is used to
accomplish joint learning for the classification of the same document
attributes. The contribution of this paper are three fold: firstly, based on
segmented word images and patches, we present a MTL based network for the
classification of a full document image. Secondly, we propose a MTL and MI
(using segmented words and patches) based combined CNN architecture ("MTL+MI")
for the classification of same document attributes. Thirdly, based on the
multi-tasking classifications of the words and/or patches, we propose an
intelligent voting system which is based on the posterior probabilities of each
words and/or patches to perform the classification of document's attributes of
complete document image.
