Handwritten letter recognition method based on lightweight deep learning network

A deep learning network and recognition method technology, which is applied in the field of handwritten letter recognition based on lightweight deep learning network and channel state information, can solve the problems of large model parameters and calculations, and is not suitable for embedded device operation. Less resource occupation, accurate feature extraction, and good efficiency

Pending Publication Date: 2022-03-25
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

The current popular convolutional neural networks, such as AlexNet, VGG, and ResNet, although the recognition effect is good, the

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  • Handwritten letter recognition method based on lightweight deep learning network
  • Handwritten letter recognition method based on lightweight deep learning network
  • Handwritten letter recognition method based on lightweight deep learning network

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Embodiment Construction

[0031] In order to make the purpose, technical effects and technical solutions of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention; obviously, the described embodiments It is a part of the embodiment of the present invention. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0032] A light-weight deep learning network handwritten letter recognition method based on channel state information provided by an embodiment of the present invention, such as figure 1 As shown, it specifically includes the following steps:

[0033] Step 1, handwritten letter data collection

[0034] Step 1.1: Use the Intel 5300 network card to collect the channel state information CSI of ordinary ...

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Abstract

The invention discloses a handwritten letter recognition method based on a lightweight deep learning network, which comprises the following steps of: processing channel state information amplitude data by adopting a signal processing technology, correcting phase data by adopting a linear change method, intercepting an effective action interval on amplitude and phase signals by using a short-time energy algorithm combined with a sliding window, and identifying the handwritten letters according to the effective action interval. A handwritten alphabet dataset combining amplitude and phase signals is established. And building a MobileNetV2 deep learning network, inputting the handwritten letter data set into a MobileNetV2 deep learning network model subjected to transfer learning for training, and obtaining a trained handwritten letter gesture classification model. The gesture classification model can be arranged on an embedded device for classification tasks. The method has the advantages of being high in accuracy, short in training time, low in equipment performance requirement and the like.

Description

technical field [0001] The invention belongs to the technical field of gesture recognition, and in particular relates to a handwritten letter recognition method based on a lightweight deep learning network and channel state information. Background technique [0002] At present, the action recognition technology based on video image technology has a good recognition effect and application scenarios, but it still has the problem that it is easily limited by the ambient light and the viewing angle of the video capture device, and it is difficult to use in relatively private places (such as bedrooms, bathrooms). In other application scenarios, there is a risk of user privacy being violated and leaked. [0003] As a popular research direction in the past two years, wireless sensing technology is not limited by light, field of view, privacy, etc., and does not require users to wear additional equipment. It has a good application prospect in the field of motion recognition in indoo...

Claims

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Application Information

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IPC IPC(8): G06V30/32G06V20/40G06N3/04G06N3/08G06V10/82
CPCG06N3/08G06N3/047G06N3/045
Inventor 戎舟施列昱王宇
Owner NANJING UNIV OF POSTS & TELECOMM
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