OCR-based Character segmentation method

Abstract

The invention relates to an OCR-based character segmentation method, which comprises the following steps: 1, acquiring a template character library based on an OCR technology, the template character library comprising standard characters and characteristic data of the standard characters; 2, identifying a part of characters in the same batch as the to-be-identified characters by using a character identification model in the OCR technology to obtain a character segmentation result, manually marking error items in the segmentation result, and updating the character identification model; 3, performing line scanning on a character to be recognized, performing initial recognition on the character to be recognized based on the updated character recognition model, and when the score of the recognition result of a certain character is smaller than a first threshold value, performing forced segmentation on the character; step 4, carrying out normalization processing; and 5, matching a certain character with a standard character according to the normalized feature data, calculating to obtain the standard character with the highest score, and determining the segmentation position of the current character based on the standard character with the highest score. The accuracy of character segmentation can be improved.

Description

technical field [0001] The invention relates to the field of optical character recognition, in particular to an OCR-based character segmentation method. Background technique [0002] In fields related to optical character recognition, such as printed text and laser marking, OCR (Optical Character Recognition, optical character recognition) plays an important role. At present, almost every product has similar information such as the production batch number. In order to ensure the traceability of the product, OCR technology is usually required. However, when actually printing text information, due to different printing environments (moving printing, printing, printing inconsistencies with different devices), there will be some situations such as text deformation, spacing changes, size changes, etc., when according to the traditional OCR character library After the model is trained for standard characters, it is easy for the model to merge two characters into one or cut one ch...

AI Technical Summary

Problems solved by technology

However, when actually printing text information, due to different printing environments (moving printing, printing, printing inconsistencies with different devices), there will be some situations such as text deformation, spacing changes, size changes, etc., when according to the traditional OCR character library After the model is trained for standard characters, it is easy for the model to merge two characters into one or cut one character into two due to the above situation, which will lead to a decrease in the recognition rate.

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