Artificial Neural Networks for Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Skew Detection in mixed Text/Graphics Documents
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Document image segmentation using discriminative learning over connected components
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
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Estimating the skew angle in text document images can be a crucial problem in optical character recognition. Based on a new sensor array processing technique, an original solution to skew angle estimation (SAE) is proposed. Thanks to the reformulation of the SAE problem in the framework of angle of arrival theory, a fast and accurate method is presented that is based on the cooperation of two neural networks. The first neural net is a three-layer perceptron receiving on input the values of the correlation matrix of the signals; the output is a "rough" estimation of the angle to estimate. This gross estimate is then used to initialize the weights of a second multi-layer perceptron (MLP). The second MLP is built in order to perform a maximum likelihood-like optimization, therefore reaching good performances. The system, though trained on simulated radar data, shows good performances on noisy handwritten texts.