Abstract
A 2D barcode is a reliable way to provide lifetime traceability of parts that are exposed to harsh environments. However, there are considerable challenges in adopting mobile cameras to read symbols directly marked on metal surfaces. Images captured by mobile cameras are usually of low quality with poor contrast due to the reflective surface of 2D barcode symbols. To deal with this problem, a novel laser-marked Data Matrix symbols reading method based on deep learning is proposed for mobile phone captured images. Utilizing the barcode module features, we train different convolutional neural network (CNN) models to learn the colors of two adjacent modules of a Data Matrix symbol. Depending on whether the colors of the two adjacent modules are the same or not, an edge image is transformed from a square grid, which is the same size as the barcode. A correction method based on the KM algorithm is used to get a corrected edge image, which helps to reconstruct the final barcode image. Experiments are carried out on our database, and the results show that the proposed algorithm outperforms in high accuracy of barcode recognition.
Highlights
Two-dimensional barcode symbols have been widely used in automated identification applications, such as industrial components retrospect [1], mobile robots [2], and Construction Safety Management [3]
The barcode images are captured by mobile phones under indoor lighting in a lab
A novel data extraction method based on local adjacent modules for images of industrial Data Matrix barcodes captured by smartphone cameras is presented
Summary
Two-dimensional barcode symbols have been widely used in automated identification applications, such as industrial components retrospect [1], mobile robots [2], and Construction Safety Management [3]. With the rapid advancement of sensing capability and computational power in consumer electronic devices, the use of mobile cameras for barcode recognition has attracted significant attention from both academia and industry [5]. The mobile device can interact with the internet systems by identifying the physical code tag. Another important application is data transfer by applying barcodes captured by mobile phones [6]. The most common printing 2D barcode symbols in daily life can be decoded rapidly and accurately by mobile phones
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