Handwritten digital recognition method and system based on depth learning

A digital recognition and handwriting technology, applied in the field of pattern recognition and machine learning, can solve the problems that affect classification performance and have a great impact on classification performance, and achieve the effects of avoiding feature extraction, short training time, and high recognition accuracy.

Inactive Publication Date: 2017-09-12
CHINA UNIV OF MINING & TECH
View PDF4 Cites 32 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the extraction of these features is too dependent on human experience and subjective consciousness. The difference in the extracted features has a great impact on the classification performance, and even the order of the extracted features will affect the final classification performance.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Handwritten digital recognition method and system based on depth learning
  • Handwritten digital recognition method and system based on depth learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0022] like figure 1 As shown, this embodiment includes the following steps:

[0023] Step 1, image preprocessing:

[0024] Step 1.1: According to possible writing conditions and different writing habits, write with deviations as much as possible to obtain images of 2000 handwritten digits;

[0025] Step 1.2: convert the handwritten digital image obtained above into a grayscale image;

[0026] Step 1.3: Normalize the image obtained in step 1.2 to a size of 28*28, and arrange the pixels into a 28*28 matrix according to the spatial position. Each matrix represents a picture and saves it in the training set, and then Make a corresponding label set based on the training set, and the 10*1 matrix represents a digital label.

[0027] Step 2, build a convolutional neural network:

[0028] The convolutional neural network used in this embodiment is a multi-layer neural network, which is composed of multiple layers such as input layer, intermediate layer and output layer. Each layer...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The present invention provides a handwritten digital recognition method and system based on depth learning. A convolutional neural network is trained with a sample set which is formed by constructing a handwritten digital picture with a label, the trained convolutional neural network is saved, and the image to be identified is taken as the input, and according to the output vector, the recognition result is obtained. The handwritten digital image is identified through the convolution neural network, so that the displayed feature extraction is avoided, the picture is directly taken as the network input, and the recognition accuracy is high. The network can be repeatedly used after being trained, the processing efficiency is high and the training time is short.

Description

technical field [0001] The invention relates to the field of pattern recognition and machine learning, in particular to a method and system for handwritten digit recognition based on deep learning. Background technique [0002] Handwritten digit recognition is the core technology for processing some data and information in daily life and industrial fields, such as: statistical reports, financial statements, zip codes, various bills, etc. As a very important branch of the field of image recognition, handwritten digit recognition is also a traditional research field of pattern recognition. It not only has great practical significance and application value, but also has extremely critical theoretical value. In practical applications, especially in the financial field, there are quite strict requirements on the accuracy of recognition, and the correctness of a single number may cause incalculable losses. [0003] Convolutional neural network is specially designed to process two...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/66G06N3/04G06N3/06
CPCG06N3/04G06N3/061G06V30/32G06V30/194
Inventor 丁世飞侯艳路
Owner CHINA UNIV OF MINING & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap