Barcode recognition method and system based on mobile terminal

Abstract

The invention provides a barcode recognition system based on a mobile terminal, which is characterized in that it includes a collection unit that collects images according to a preset pixel range, and stores the collected images; the recognition unit, The collected image is analyzed according to a preset barcode analysis rule to obtain a usable barcode; the decoding unit decodes according to the obtained barcode.

Description

technical field [0001] The invention relates to a bar-dimensional code identification technology, in particular to a mobile terminal-based bar-dimensional code identification method and a system thereof. Background technique [0002] Currently, scanning recognition software with barcodes and two-dimensional codes has been widely used in mobile terminals with camera functions. The usual method is: use the camera on the mobile terminal to aim at the object to be identified, and the code scanning software will continuously collect the picture during the camera scanning process, and analyze the picture according to the corresponding coding rules, and display the final calculation result after success. [0003] The general scanning process is divided into the following three processing units: image acquisition, image recognition and information display, such as figure 1 As shown, the image recognition can be divided into four parts: image preprocessing, area positioning, barcode...

AI Technical Summary

Problems solved by technology

[0004] However, in the process of using the mobile terminal to scan codes, we often encounter such situations: when the ambient light is relatively dark or bright, it is difficult to distinguish the recognized object, or the light is blocked, resulting in shadows on the object, such as Figure 2A Shown; the camera is too close to the subject and cannot focus, or too far away from the subject and the image is small, such as Figure 2B As shown; there is an inclination or rotation angle between the plane where the camera is located and the plane where the object is located, causing the object to be distorted and deformed in the captured image, such as Figure 2C As shown; There are multiple clear barcodes in the scanning frame, and it is impossible to determine which barcode is the result after scanning, such as Figure 2D As shown; due to different user software operation methods or equipment factors, the obtained object image is not clear enough, etc. These situations often lead to repeated retries in the process of scanning codes, which makes the recognition efficiency low and affects the user experience.

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