Model training method, watermark text recognition method, and related device

A watermark restoration method, comprising:

• acquiring a target image, and extracting color values in pixel blocks of the target image;
• processing the color values by calling a pre-trained watermark restoration model to obtain a watermark restoration image corresponding to the target image;
• wherein, the watermark restoration model is trained based on a watermark image set synthesized by pre-defined watermark style information and background style information, and the watermark restoration model takes the color value of the pixel block corresponding to the image as an input of the model, and takes an image subjected to pixelation watermark restoration as an output;
• and
• removing a blank and/or overlap in the watermark restoration image corresponding to the target image by a heuristic search algorithm to obtain a watermark restoration result corresponding to the target image.

An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, the processor, when executing the program, implements a training method for a watermark restoration model, comprising:

• acquiring watermark style information and background style information, the watermark style information being used for indicating a visible-watermark character content style;
• and the background style information being used for indicating a background image content style;
• generating a watermark image set according to a combination of the watermark style information and the background style information, the watermark image set comprising a plurality of images with visible watermarks;
• performing pixelation processing on the watermark images in the watermark image set. extracting pixel values in pixel blocks as training samples, using the visible watermarks corresponding to the watermark images as sample labels, and combining the training samples and the sample labels corresponding to the training samples to generate a training data set;
• and
• constructing a bidirectional recurrent neural network model, and training the bidirectional recurrent neural network model by calling the training data set, so as to obtain a bidirectional training neural network model meeting a training termination condition as a watermark restoration model, the watermark restoration model being used for restoring visible-watermark characters in the images.