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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Jobin, K.V., Jawahar, C.V. (2018). Document Image Segmentation Using Deep Features. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_33
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DOI: https://doi.org/10.1007/978-981-13-0020-2_33
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