Fusion of Visual and Textual Features for Table Header Detection in Handwritten Text Images

This paper introduces a new procedure to improve table header detection in handwritten text images from the fusion of the posterior probabilities provided by two baseline classifiers. Each classifier considers a different modality, namely visual or textual features. Both baseline classifiers implements convolutional neural networks, particularly adopting the U-Net architecture. Four fusion methods are considered: the mean; linear discriminant analysis and random forest as meta-classifiers; and a recently developed method called alpha integration. The testing dataset consisted of 89 page images drawn from the Passau dataset. The improved performance provided by the fusion methods in the specific experiments is interesting considering the complexity of the challenging problem approached. In terms of area under the receiver operating characteristic curve the best results were obtained by alpha integration. This method incorporates least mean square parameter optimization. The improvement is relevant in the context of the targeted problem.