Region labeling method, device and equipment and storage medium

Abstract

The embodiment of the invention discloses a region labeling method, device and equipment in the field of artificial intelligence and a storage medium. The method comprises the steps: acquiring N labeling points labeled for the boundary of a coverage region of a target curved text; determining four corner points of a to-be-labeled target curved region in the N labeling points; according to the fourcorner points, selecting marking points used for fitting a first curve from the N marking points to form a first marking point set, selecting marking points used for fitting a second curve from the Nmarking points to form a second marking point set, wherein the first curve and the second curve are two curve boundaries opposite to the target curved area; fitting a first curve according to the marking points in the first marking point set, and fitting a second curve according to the marking points in the second marking point set; and constructing a target curved region based on the first curveand the second curve. According to the method, the annotation quality of the curved text annotation area can be improved, and the annotation time cost can be reduced.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence (AI), in particular to an area labeling method, device, equipment and storage medium. Background technique [0002] Optical Character Recognition (OCR) is an important hot research issue in the field of computer vision. As one of the important applications of OCR technology, curved text recognition is used to recognize text characters distributed in a curved shape. In recent years, with the rapid development of deep learning technology in the field of image processing, OCR based on deep learning has become a mainstream trend. Deep learning usually requires a large amount of labeled data to train the processing model. For the model used to realize curved text recognition, it is often necessary to use a large number of samples marked with curved text areas to train it. [0003] The current mainstream method for labeling curved text areas is based on the open source tool La...

AI Technical Summary

Problems solved by technology

like figure 1 As shown, the more labeling points marked by the labeler and the denser the distribution of labeling points, the closer the polygon generated by LabelMe (that is, the labeling result of the curved text area) is closer to the ideal labeling result, but the cost of labeling time is higher; on the contrary, The fewer the labeling points marked by the labeler and the sparser the distribution of the labeling points, the lower the cost of labeling time and cost. However, the deviation between the polygon generated by LabelMe and the ideal labeling result is large, which is not conducive to subsequent model training.

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