Data processing method, electronic equipment and computer readable medium

Abstract

The embodiment of the invention discloses a data processing method, electronic equipment and a computer readable medium, and the method comprises the steps: carrying out the text detection of a firsttext image, and obtaining the information of a text region in the first text image; according to the information of the text area, performing image interception on the first text image to obtain a corresponding first intercepted image which does not contain a text; obtaining a plurality of text sentences, and respectively fusing the plurality of text sentences with the first intercepted image to obtain a plurality of second text images; and constructing a training sample for training a text recognition model by taking the plurality of second text images as sample images and taking text contents of text sentences corresponding to the second text images as text annotations of the second text images. Through the embodiment of the invention, the construction efficiency of the training sample for training the text recognition model is improved.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of computers, and in particular, to a data processing method, electronic equipment, and a computer-readable medium. Background technique [0002] With the development of machine learning technology, neural network models have made great progress in various applications. For example, neural network models are currently widely used in speech recognition, text recognition, and so on. [0003] Although in many respects, the recognition accuracy of the neural network model based on machine learning technology is already quite accurate. However, machine learning has natural limitations, for example, a large amount of training data is required to train the neural network model, a large amount of data processing is required, and so on. At present, the commonly used method of obtaining training data is to manually collect data and manually mark it to form training data. The larger the scal...

AI Technical Summary

Problems solved by technology

[0005] It can be seen from the above that the existing neural network models either have the problem of low training efficiency of the neural network model due to manual collection and labeling of training data; or the problem of low data processing efficiency due to the large amount of data to be processed

But no matter what kind of problem it is, it will affect the processing efficiency of the neural network model as a whole.

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