Multi-dimensional Speaker Information Recognition with Multi-task Neural Network

2018 ◽  
Author(s):  
Haixia Chen ◽  
Longting Xu ◽  
Zhen Yang
Keyword(s):  
Neural Network ◽  
Speaker Information ◽  
Information Recognition

Research on Data Acquisition Algorithms Based on Image Processing and Artificial Intelligence

2019 ◽  
Vol 34 (06) ◽  
pp. 2054016
Author(s):  
Shuyu Hu
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Data Acquisition ◽  
Bp Neural Network ◽  
Decisive Role ◽  
Recognition Algorithm ◽  
Important Research Topic ◽  
Information Recognition ◽  
Handwritten Signature ◽  
Interference Background

At present, image recognition processing technology has been playing a decisive role in the field of pattern recognition, of which automatic recognition of bank notes is an important research topic. Due to the limitation of the size of bill layout and printing method, many invoice layouts are not clear, skewed or distorted, and even there are irregular handwritten signature contents, which lead to the problem of recognition of digital characters on bill surface. In this regard, this paper proposes a data acquisition and recognition algorithm based on improved BP neural network for ticket number identification, which is based on the theory of image processing and recognition, combined with improved bill information recognition technology. First, in the pre-processing stage of bill image, denoising and graying of bill image are processed. After binarization of bill image, the tilt detection method based on Bresenham integer algorithm is used to correct the tilted bill image. Secondly, character localization and feature extraction are carried out for par characters, and the target background is separated from the interference background in order to extract the desired target characters. Finally, the improved BP neural network-based bill digit data acquisition and recognition algorithm is used to realize the classification and recognition of bill characters. The experimental results show that the improved method has better classification and recognition effect than other data acquisition and recognition algorithms.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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