Nameplate recognition method, computer equipment and storage medium
A nameplate, to-be-recognized technology, applied in the field of natural text recognition, can solve problems such as slowing down model recognition speed, unfavorable recognition of text content, and non-intensive text information, so as to improve text recognition rate, improve training speed, and expand the effect of receptive field
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Embodiment 1
[0055] A nameplate automatic identification method, comprising:
[0056] The image to be recognized is sent to the classification network model to obtain the direction angle of the image to be recognized, and the direction of the image to be recognized is corrected; the generation method of the classification network model includes: adjusting several nameplate pictures obtained to the level (obtaining the nameplate image used for training, by The user shoots by himself. Due to the influence of environmental factors during the image collection process, the shooting angle may not be fixed, and the obtained nameplate image will appear blurred and deformed; the nameplate part in the image to be recognized is manually adjusted to the level to obtain the training data set ); randomly rotate several fixed angles; the classification network model is obtained by training the nameplate pictures marked with fixed angles of rotation; several fixed angles include 0°, 45°, 90°, 135°, 180°, 2...
Embodiment 2
[0061] According to a kind of nameplate automatic identification method described in embodiment 1, its difference is:
[0062] Such as figure 2 As shown, the text area detection module refers to the CTPN (ConnectionistTextProposalNetwork) network. CTPN combines CNN and LSTM deep network to effectively detect horizontally distributed text in complex scenes. The text area detection module is obtained by training the nameplate image containing the label. ,Refers to:
[0063] First, use the VGG16 classification model to extract features and obtain a feature map with a size of N×C×H×W; N, C, H, and W refer to BatchSize, number of feature map channels, feature map height, and feature map width respectively; in N A 3×3 sliding window is made on the feature map of ×C×H×W, and the output of the feature map of N×(9*C)×H×W is obtained, and each point (each along the direction of height and width positions) are combined with 3×3 regional features, and the feature map of N×(9*C)×H×W is r...
Embodiment 3
[0068] According to a kind of nameplate automatic identification method described in embodiment 1 or 2, its difference is:
[0069] Such as image 3 As shown, the text recognition module includes sequentially connected STN space transformation network, feature extraction module and time convolution network;
[0070] The STN space transformation network is used to offset the influence of the image due to the incorrect shooting angle, the feature extraction module is used to extract the visual features of the text image, and the temporal convolution module extracts the text semantic features corresponding to the text image.
[0071] The text recognition module is obtained through training, including the following steps:
[0072] First of all, for the nameplate picture marked with the text image area, it is scaled to 32×320, and the STN (SpatialTransformerNetwork) spatial transformation network is used to perform adaptive affine transformation on the text image scaled to the sta...
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