Bilingual text detection from natural scene images using faster R-CNN and extended histogram of oriented gradients

Pattern Analysis and Applications - In today's world, scene text detection is important for a wide range of scientific and industrial processes. Compared with text detection in documents, text...     

Accurate Computation of Vocal Tract Filter Parameters Using a Hybrid Genetic Algorithm

This paper reports an original technique for accurately estimating the parameters of human vocal tract filters for vowels in English for speech processing applications such as voice recognition. In this paper, the vocal tract filter design problem is reformulated as a general nonlinear optimization problem and solved using a hybrid Genetic Algorithm (GA). The hybrid GA computes a rough estimate of the global minimum using GA and refines using computationally cheap local search. Issues that are of concern in digital filtering such as achieving stability and overcoming finite precision effects are addressed. The objective function for optimization used in this paper is formulated in terms of poles and zeros of the filters to avoid ill-conditioning and to take advantage of symmetries in the location of poles and zeros. Simulation results indicate that the approach presented in this paper provides a close fit in terms of mean square error between the experimental and designed filters.