Text detection method, electronic equipment and computer readable medium
A text detection and text technology, applied in the computer field, can solve the problems of long time consumption, large amount of calculation, and large consumption of computing resources, etc., and achieve the effect of improving calculation speed, fast processing speed, and high boundary determination efficiency
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Embodiment 1
[0020] Referring to FIG. 1 , it shows a flowchart of steps of a text detection method according to Embodiment 1 of the present invention.
[0021] The text detection method of the present embodiment comprises the following steps:
[0022] Step S102: performing feature extraction and image segmentation on the text image to be detected, at least obtaining a text region probability map of the text image to be detected and image features of the text image to be detected.
[0023] The solutions of the embodiments of the present invention are applicable to text detection with various text densities, including but not limited to regular density text, dense density text, sparse density text, especially dense density text. Among them, the specific indicators for determining whether a certain text is a dense text can be appropriately set by those skilled in the art according to the actual situation, including but not limited to: according to the spacing between texts (such as spacing le...
Embodiment 2
[0057] refer to figure 2 , shows a schematic flowchart of a text detection method according to Embodiment 2 of the present invention.
[0058] The text detection method of the present embodiment adopts such as Figure 1D The neural network model implementation shown in , the text detection method includes the following steps:
[0059] Step S202: Input the text image to be detected into the Resnet18 network.
[0060] In this embodiment, the Resnet18 network, as a part of the PAN, is a trained network through which the features of the input image can be extracted, and the features of a certain channel form the feature map of the channel.
[0061] Step S204: Perform feature extraction through the Resnet18 network.
[0062] In order to distinguish it from the subsequent feature extraction, the feature extraction in this step is marked as extraction feature 1, through which the texture, edge, corner and semantic information of the text image to be detected can be extracted.
...
Embodiment 3
[0095] image 3 It is the hardware structure of the electronic equipment in the third embodiment of the present invention; as image 3 As shown, the electronic device may include: a processor (processor) 301 , a communication interface (Communications Interface) 302 , a memory (memory) 303 , and a communication bus 304 .
[0096] in:
[0097] The processor 301 , the communication interface 302 , and the memory 303 communicate with each other through the communication bus 304 .
[0098] The communication interface 302 is used for communicating with other electronic devices or servers.
[0099] The processor 301 is configured to execute the program 305, and specifically, may execute relevant steps in the above text detection method embodiment.
[0100] Specifically, the program 305 may include program codes including computer operation instructions.
[0101] The processor 301 may be a central processing unit CPU, or an ASIC (Application Specific Integrated Circuit), or one o...
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