A selectional auto-encoder approach for document image binarization
This addresses the problem of robust binarization for document analysis systems, which is crucial for automatic information retrieval, though it appears incremental as it applies neural networks to an existing task.
The paper tackles document image binarization by proposing a convolutional auto-encoder that learns an end-to-end map from input images to pixel likelihoods for foreground/background, outperforming existing methods across multiple document domains.
Binarization plays a key role in the automatic information retrieval from document images. This process is usually performed in the first stages of documents analysis systems, and serves as a basis for subsequent steps. Hence it has to be robust in order to allow the full analysis workflow to be successful. Several methods for document image binarization have been proposed so far, most of which are based on hand-crafted image processing strategies. Recently, Convolutional Neural Networks have shown an amazing performance in many disparate duties related to computer vision. In this paper we discuss the use of convolutional auto-encoders devoted to learning an end-to-end map from an input image to its selectional output, in which activations indicate the likelihood of pixels to be either foreground or background. Once trained, documents can therefore be binarized by parsing them through the model and applying a threshold. This approach has proven to outperform existing binarization strategies in a number of document domains.