Text recognition method and device
A text recognition and text technology, applied in the computer field, can solve the problem of low text detection accuracy and achieve the effect of improving text detection rate and accuracy
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Embodiment 1
[0022] refer to figure 1 , shows a flow chart of steps of a text recognition method according to Embodiment 1 of the present invention.
[0023] The text recognition method of the present embodiment comprises the following steps:
[0024] Step S102: Obtain a text image to be detected.
[0025] Wherein, the text image to be detected contains information of a plurality of characters, and the characters include but not limited to one or more of characters, symbols, numbers, and letters.
[0026] In the embodiment of the present invention, the text image to be detected is an image of a question title. But not limited thereto, other text images are also applicable to the solution of this embodiment of the present invention.
[0027] Step S104: Perform multi-scale transformation on the text image to be detected to obtain multiple sub-text images of different sizes.
[0028] Multi-scale image technology, also known as multi-resolution technology (MRA), refers to the use of multi-...
Embodiment 2
[0040] refer to figure 2 , shows a flow chart of steps of a text recognition method according to Embodiment 2 of the present invention.
[0041] The text recognition method of the present embodiment comprises the following steps:
[0042] Step S202: training a convolutional neural network model for text detection on text images.
[0043] This step is optional. As mentioned above, in actual use, the convolutional neural network model trained by a third party can also be directly used for text detection.
[0044] During training, a large number of text images can be automatically generated using methods such as freetype and pygame based on the existing topic text data; image blurring, image rotation, scaling, distortion and other geometric deformations, image contrast transformation, image noise pollution, etc. One or more of the techniques enhances the diversity of text images. Thus, a large batch of sample images used for training the convolutional neural network model is ...
Embodiment 3
[0104] refer to Figure 8 , shows a structural block diagram of a text recognition device according to Embodiment 3 of the present invention.
[0105] The text recognition device of this embodiment includes: a first acquisition module 302, configured to acquire a text image to be detected, wherein the text image contains information of a plurality of characters; a second acquisition module 304, configured to Perform multi-scale transformation of the text image to obtain multiple sub-text images of different sizes; the third acquisition module 306 is used to perform text detection on each sub-text image using a convolutional neural network model, and obtain each character in each sub-text image Corresponding candidate text detection frame; determination module 308, for carrying out non-maximum suppression NMS processing to multiple candidate text detection frames of all sub-text images of the same character, and filtering the processed candidate text detection frame, Determine...
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