Multi-layer Feature Extraction Research Articles

Multilingual Text Detection with Nonlinear Neural Network

Multilingual text detection in natural scenes is still a challenging task in computer vision. In this paper, we apply an unsupervised learning algorithm to learn language-independent stroke feature and combine unsupervised stroke feature learning and automatically multilayer feature extraction to improve the representational power of text feature. We also develop a novel nonlinear network based on traditional Convolutional Neural Network that is able to detect multilingual text regions in the images. The proposed method is evaluated on standard benchmarks and multilingual dataset and demonstrates improvement over the previous work.

Boosting neural network feature extraction by reduced accuracy activation functions

The paper proposes the use of a lookup table in the computation of the sigmoid function in multilayer perceptron feature extraction. The approach is frequently used in limited precision hardware implementations. This paper considers its use as a software implementation. It is argued that the method is computationally efficient and does not compromise the performance of the neural networks. This new approach is analysed and evaluated in both theoretical and experimental terms. The practical study is performed using several real world and synthetic data sets. The results show that an increase in computational efficiency is achieved with very little or no influence on the quality of the extracted features.