Offset Neural Network for Document Orientation Identification
Ruochen Wang, Song Wang, Jun Sun · 2018
With advancements in deep learning, artificial neural networks have been used increasingly in various document analysis problems such as character recognition, layout analysis, and orientation identification of documents. However, because of the ambiguity of the document image (caused by complicated appearances, multiple languages, etc.), it is difficult to use Convolutional Neural Networks (CNN) directly for orientation identification of the document. In order to solve this problem, we present offset neural networks (ONN), a new type of neural network that is especially designed for orientation identification. The ONN successfully reduces the negative influence of the ambiguous parts whose orientation cannot be distinguished. Meanwhile, the distinguishable parts of the document can be enhanced, which further improves the performance of the whole model. In the experiment, ONN shows better performance and robustness compared with the ordinary CNN. Especially for some extreme cases, the ONN is still able find the correct orientation. Considering that no one has ever proposed a dedicated neural network for orientation identification, our work is very practical and innovative.