An ISBN Code Number Recognition Algorithm Based on Android

2015 ◽  
Author(s):  
Ping Feng ◽  
Dawei Xu ◽  
Xiaoli Wang
Keyword(s):  
Recognition Algorithm ◽  
Code Number ◽  
Number Recognition

Handwritten Character Recognition Research Based on Adaptive Minimum Distance Classifiers Integration

2010 ◽  
Vol 44-47 ◽  
pp. 3388-3392
Author(s):  
Zhao Ping Tang ◽  
Jian Ping Sun ◽  
Lu Sheng Zhong
Keyword(s):  
Character Recognition ◽  
Minimum Distance ◽  
Classification Problem ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Financial Statement ◽  
Number Recognition ◽  
Rbf Kernel ◽  
Rbf Kernel Function ◽  
Self Learning

It is difficulty to gain completely satisfactory effect if a single classification is used to check a complicated recognition classification problem. Using the complementarities between the different classify method, integrating many classifiers, it can reduce the identification mistake and strengthen recognition robustness. Taking a offline handwritten number recognition system as an example, adopting Bayesian discriminate function based on minimal mistake rate, uniting recognition algorithm of RBF kernel function, using Bagging technology, adaptive minimum distance classifiers integration is designed in which there is minimal mistake rate. Furthermore, an offline handwritten number recognition system in high accuracy is exploited in which there is adaptive and self-learning function. It can be used for important economic fields such as financial statement and bank paper.

scholarly journals Implementation of a number recognition algorithm built using a neural network on the BASYS3 FPGA panel

2021 ◽  
Vol 82 (4) ◽  
pp. 86-96
Author(s):  
A. A. Mukhanbet ◽  
◽  
E. S. Nurakhov ◽  
B. S. Daribayev ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Power Consumption ◽  
Low Power ◽  
Recognition Algorithm ◽  
Low Power Consumption ◽  
Portable Devices ◽  
Output Performance ◽  
Programmable Valve ◽  
Number Recognition ◽  
Field Programmable

In recent years, some field programmable valve arrays (FPGAs) based on CNN release phase accelerators have been introduced. FPGA is widely used in portable devices. They can be programmed to achieve higher concurrency and provide better performance. The power consumption of the FPGA is lower than that of GPUs with the same workload. These reasons make the FPGA suitable for implementing the CNN release phase. They can provide relative output performance for GPUs and achieve low power consumption, which is very important for portable devices. To effectively implement the CNN output phase on the FPGA, the design should have high parallelism, and the hardware resources used should be minimized to reduce the area and power consumption. In the process of working with the help of a neural network, an algorithm for recognizing handwritten numbers is implemented. A special architecture is being created to implement a neural network at the appatent level. The performance during operation and power consumption is comparable to the performance of the processor and the GPU.

Recognition Analysis of Wood Flour Mesh Number Based on External Rectangle Fitting Algorithm

2014 ◽  
Vol 496-500 ◽  
pp. 1995-1998 ◽  
Author(s):  
Hong E Ren ◽  
Mian Liu ◽  
Meng Zhu
Keyword(s):  
Wood Flour ◽  
Morphological Characteristics ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Detection Methods ◽  
Microscopic Images ◽  
Boundary Points ◽  
Number Recognition ◽  
Fitting Algorithm ◽  
Fitting Accuracy

To overcome disadvantages of traditional detection methods of wood flour mesh number, a mesh number recognition algorithm based on external rectangle fitting and morphological characteristics has been studied. It makes use of minimum external rectangle with the boundary points obtained by the preprocessing of microscopic images. The external rectangles length is calculated when the area is the smallest. The experimental results demonstrate that the proposed algorithm has a good fitting accuracy and meets producing demands.

New Banknote Number Recognition Algorithm Based on Support Vector Machine

2013 ◽  
Author(s):  
Shan Gai ◽  
Guowei Yang ◽  
Sheng Zhang ◽  
Minghua Wan
Keyword(s):  
Support Vector Machine ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Number Recognition

Pavement Crack Recognition Algorithm Based on Multi-Scale Shape Feature and BP Neural Network

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Yunchao Li ◽  
Zhigang Xu ◽  
Zijun Jiang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Algorithm ◽  
Shape Feature ◽  
Multi Scale ◽  
Crack Recognition
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