Extraction Algorithm of English Text Information From Color Images Based on Radial Wavelet Transform
Yaqin Wang · IEEE Access · 2020
The Chinese and English text information of color image reflects some important contents of color image to a certain extent, and these texts are extracted automatically, which has important application value in the fields of information retrieval, digital library, web page retrieval and intelligent transportation. In this paper, various signal extraction techniques based on radial wavelet transform modulus Maxima are analyzed, and it is found that these techniques have poor ability to extract weak signals, and the higher requirements for directionality lead to pseudo-boundary phenomena in two-dimensional image extraction results. Based on the correlation denoising method of radial wavelet coefficients, the radial wavelet entropy is introduced into the field of signal extraction, and the complex Morlet radial wavelet is selected as the basis function. A complex Morlet radial wavelet entropy extraction algorithm suitable for extracting the English text characteristics of weak signal color images is proposed. In addition, a method of scene text recognition is proposed. Based on D-SIFT local features and spatio-temporal histogram, this method vectorizes text samples in the framework of Bo Ws model. Because the spatio-temporal histogram can flexibly model the structural information of the text, this method can effectively describe the scene text. On this basis, the selective ensemble learning method is used to improve the performance of the text extractor. In order to expand the application range of the algorithm and improve the running efficiency of the integrated extractor, a model compression method based on color image English text samples and integrated extractor is proposed. The integrated extractor which takes up more space and runs slowly is compressed into an equivalent and more efficient one. In order to reduce the number of pseudo-samples needed in the process of model compression, a method of training local extractor based on color image English text samples is proposed, which greatly reduces the number of pseudo-samples needed and improves the efficiency of the model compression method.