OCR image sample generation method and device, printed matter verification method and device, equipment and medium

Abstract

The invention relates to artificial intelligence, and provides an OCR image sample generation method and device, a printing form verification method and device, equipment and a medium, and the methodcomprises the steps: receiving an image generation instruction, and obtaining an image sample; inputting the image sample into a preset font typesetting generation model, obtaining first annotation information by performing text detection and character recognition on the image sample, and obtaining a simulation result generated by reconstruction of the font typesetting generation model; inputtingthe image sample and the simulation image into a preset style synthesis model, extracting style features and content features by the style synthesis model, and generating a synthesis result by the style synthesis model; and obtaining an OCR image sample label, recording the synthesized image as an OCR image sample corresponding to the image sample, and associating the OCR image sample with the OCRimage sample label. In addition, the invention also relates to a blockchain technology, and the information can be stored in the blockchain node. According to the invention, the OCR image sample withthe same texture style as the image sample is automatically generated, and the sample label is automatically labeled.

Description

technical field [0001] The invention relates to the field of data modeling of artificial intelligence, in particular to a method, device, computer equipment and storage medium for OCR image sample generation and print verification. Background technique [0002] At present, with the development of society's science and technology, and the continuous expansion of the digital age, the application of text recognition based on OCR technology has been widely used; OCR (Optical Character Recognition), Chinese called optical character recognition, for the use of optical Words written on or on paper are read and converted into a format that a computer can accept and understand. In the existing technology, more and more application scenarios (such as: application scenarios involving finance, insurance, and smart security) need to identify the text information in the printed document for verification, but because it requires a very large However, usually only a very small number of do...

AI Technical Summary

Problems solved by technology

In the existing technology, more and more application scenarios (such as: application scenarios involving finance, insurance, and smart security) need to identify the text information in the printed document for verification, but because it requires a very large However, usually only a very small number of document samples can be obtained, and it is difficult to obtain such a large number of document samples, and due to its unique interference (such as Fonts may become broken or ink sticks during the printing process) making it difficult to perform OCR recognition, resulting in low accuracy and precision of the trained document recognition model, resulting in a high verification error rate and requiring manual re-verification, which greatly wastes costs ,low efficiency

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