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A Sparse and Locally Shift Invariant Feature Extractor Applied to Document Images

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2 Author(s)
Ranzato, M. ; New York Univ., New York ; LeCun, Y.

We describe an unsupervised learning algorithm for extracting sparse and locally shift-invariant features. We also devise a principled procedure for learning hierarchies of invariant features. Each feature detector is composed of a set of trainable convolutional filters followed by a max-pooling layer over non-overlapping windows, and a point-wise sigmoid non-linearity. A second stage of more invariant features is fed with patches provided by the first stage feature extractor, and is trained in the same way. The method is used to pre-train the first four layers of a deep convolutional network which achieves state-of-the-art performance on the MNIST dataset of handwritten digits. The final testing error rate is equal to 0.42%. Preliminary experiments on compression of bitonal document images show very promising results in terms of compression ratio and reconstruction error.

Published in:

Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on  (Volume:2 )

Date of Conference:

23-26 Sept. 2007