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STDP-based pulse neural network handwritten Chinese character recognition method

A spiking neural network and Chinese character recognition technology, applied in the field of image recognition, can solve the problem of low efficiency of handwritten Chinese character recognition

Pending Publication Date: 2020-02-25
GUANGDONG UNIV OF TECH
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Problems solved by technology

[0005] The present invention provides a handwritten Chinese character recognition method based on STDP in order to overcome the low efficiency of handwritten Chinese character recognition described in the prior art

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  • STDP-based pulse neural network handwritten Chinese character recognition method
  • STDP-based pulse neural network handwritten Chinese character recognition method
  • STDP-based pulse neural network handwritten Chinese character recognition method

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Embodiment 1

[0047] The present embodiment provides a kind of STDP-based pulse neural network handwritten Chinese character recognition method, such as figure 1 As shown, the method includes the following steps:

[0048] S1: Download the HWDB1.1 offline data set in the Chinese Academy of Sciences CASIA handwritten Chinese database;

[0049] S2: Preprocessing the offline handwritten Chinese character dataset: the size of each picture in the dataset is different, and it is impossible to uniformly put the pictures into the input layer and compile them into pulse sequences, so the pictures need to be normalized, and the unified size is 64*64 pixels.

[0050] S3: Determine the number of neurons used for training: In the offline data set, the N class labels {z 1 ,z 2 ,…z n}, Each type of label adopts ISODATA unsupervised learning for similarity clustering, and the IOSDATA similarity clustering algorithm is as follows figure 2 As shown, the main steps are divided into three steps: S3.1: Ini...

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Abstract

The invention relates to an STDP-based pulse neural network handwritten Chinese character recognition method, which comprises the following steps of S1, downloading an offline data set, i.e., an offline handwritten Chinese character data set; S2, preprocessing the offline data set: performing normalization processing on each picture in the data set; S3, determining the number of neurons for training; S4, constructing a network structure; S5, performing pulse coding on each pixel in the neural network; S6, determining a neuron model; S7, learning the neuron model by adopting an STDP learning rule; S8, putting the data sets into the network in sequence for training, and completing the training of the pulse neural network after iterating for three times. The recognition method can improve therecognition efficiency of the handwritten Chinese characters. An STDP learning mechanism adopted in the invention exists in conoid neurons of hippocampus at the earliest, and the relative timing sequence of pulse distribution before and after synapsis induces different synapsis change processes, so that the membrane potential of neurons is influenced.

Description

technical field [0001] The present invention relates to the technical field of image recognition, and more specifically, relates to a method for recognizing handwritten Chinese characters based on an STDP-based pulse neural network. Background technique [0002] For a long time, the problem of Handwritten Chinese Character Recognition (HCCR) has attracted extensive attention and research, and plays an important role in various applications. Such as bank check recognition, automatic mail sorting, document digitization, intelligent education, etc. Previous handwritten Chinese character recognition work can be divided into different types, including recognition tasks such as numbers, English characters, Chinese characters, and French characters. The HCCR problem has been extensively studied for more than 40 years and can be further divided into two categories: online recognition and offline recognition. Online recognizers use the digitized trace of the pen to recognize charac...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V30/32G06F18/23G06F18/2155G06F18/214
Inventor 刘家华陈靖宇
Owner GUANGDONG UNIV OF TECH
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