671 research outputs found
Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images
There are two types of information in each handwritten word image: explicit
information which can be easily read or derived directly, such as lexical
content or word length, and implicit attributes such as the author's identity.
Whether features learned by a neural network for one task can be used for
another task remains an open question. In this paper, we present a deep
adaptive learning method for writer identification based on single-word images
using multi-task learning. An auxiliary task is added to the training process
to enforce the emergence of reusable features. Our proposed method transfers
the benefits of the learned features of a convolutional neural network from an
auxiliary task such as explicit content recognition to the main task of writer
identification in a single procedure. Specifically, we propose a new adaptive
convolutional layer to exploit the learned deep features. A multi-task neural
network with one or several adaptive convolutional layers is trained
end-to-end, to exploit robust generic features for a specific main task, i.e.,
writer identification. Three auxiliary tasks, corresponding to three explicit
attributes of handwritten word images (lexical content, word length and
character attributes), are evaluated. Experimental results on two benchmark
datasets show that the proposed deep adaptive learning method can improve the
performance of writer identification based on single-word images, compared to
non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio
Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks
Handwritten character recognition has been the center of research and a
benchmark problem in the sector of pattern recognition and artificial
intelligence, and it continues to be a challenging research topic. Due to its
enormous application many works have been done in this field focusing on
different languages. Arabic, being a diversified language has a huge scope of
research with potential challenges. A convolutional neural network model for
recognizing handwritten numerals in Arabic language is proposed in this paper,
where the dataset is subject to various augmentation in order to add robustness
needed for deep learning approach. The proposed method is empowered by the
presence of dropout regularization to do away with the problem of data
overfitting. Moreover, suitable change is introduced in activation function to
overcome the problem of vanishing gradient. With these modifications, the
proposed system achieves an accuracy of 99.4\% which performs better than every
previous work on the dataset.Comment: 5 pages, 6 figures, 3 table
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