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2018 | OriginalPaper | Chapter

Document Image Segmentation Using Deep Features

Authors : K. V. Jobin, C. V. Jawahar

Published in: Computer Vision, Pattern Recognition, Image Processing, and Graphics

Publisher: Springer Singapore

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Abstract

This paper explores the effectiveness of deep features for document image segmentation. The document image segmentation problem is modelled as a pixel labeling task where each pixel in the document image is classified into one of the predefined labels such as text, comments, decorations and background. Our method first extracts deep features from superpixels of the document image. Then we learn an svm classifier using these features, and segment the document image. Fisher vector encoded convolutional layer features (fv-cnn) and fully connected layer features (fc-cnn) are used in our study. Experiments validate that our method is effective and yields better results for segmenting document images in comparison to the popular approaches on benchmark handwritten datasets.

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Title: Document Image Segmentation Using Deep Features
Authors: K. V. Jobin
C. V. Jawahar
Publisher: Springer Singapore
Book: Computer Vision, Pattern Recognition, Image Processing, and Graphics
Print ISBN: 978-981-13-0019-6

Electronic ISBN: 978-981-13-0020-2

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-981-13-0020-2_33

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