![]() If open source is a must for your project, it’s a safer choice to go with BoofCV or OpenCV WeChat. Note: The reading rates of some categories are not very high, because the QR codes are in bad conditions and not readable. It ranks 2nd in the noncompliant and perspective categories and 4th in the lots category. In some categories, for example, the blurred, curved, glare, and high version, the SDK is the only solution that has a rate higher than 67%. We can see that the Dynamsoft Barcode Reader ranks 1st in most of the categories. Non-compliant QR Codes have customized designs, for example, colors, or a logo at the center.ĭynamsoft Barcode Reader Wins by a Large Margin.The Version Information represents the number of cells (per side) making up the code. The QR Code symbology ranges from Version 1 to Version 40.The lots category has multiple QR Codes in one image.The markers are designed for detection of QR Codes. Pathological QR codes have the markers intentionally corrupted.Damaged QR codes are the ones with scratches, dents, etc.time () elapsedTime = int (( end_time - start_time ) * 1000 ) Detection Results img_folder, filename )) end_time = time. We can get the reading rate by dividing detected QR codes by total QR codes in images. The performance is evaluated by reading rate and runtime. WeChat QR code detector in OpenCV (version: OpenCV 4.5.3, open-source).Zbar (version: pyzbar 0.1.8, open-source).BoofCV (version: pyboof 0.36.1, open-source).Commercial SDK B (version: Windows 5.19.3.10, commercial).Commercial SDK A (version: Java 13.5, commercial).Dynamsoft Barcode Reader (DBR, version: 8.6, commercial).We are going to evaluate 7 libraries and SDKs: It is going to run on a PC device with an Intel i5-10400 CPU and 16GB memory. The tool uses Python to run the test and provides a web interface. In order to run the benchmark, a performance test tool is written. What are the Best Data Matrix Reading SDKs? This article is Part 1 in a 4-Part Series.ġD Barcode Scanning Accuracy Benchmark and Comparison In this article, we are going to do a QR code reading benchmark using this dataset. The dataset has 536 images containing 1232 QR codes and has 16 categories: blurred, bright_spots, brightness, close, curved, damaged, glare, high_version, lots, monitor, nominal, noncompliant, pathological, perspective, rotations and shadows. A performance test is then made on the dataset to evaluate 5 open-source libraries. The author collected a dataset of QR code images and put them into categories. A comprehensive one is made by the author of the open-source computer vision library Boofcv. There aren’t many QR codes reading benchmarks. A benchmark is needed to evaluate which one is more robust or suitable for a specific use case. There are many open-source and commercial libraries or SDKs which can read QR codes. For example, the following QR code is damaged and wrinkled, which is difficult to read. Reading QR codes in the real world is a challenging job. It can be captured using a camera and then be decoded using image processing methods. ![]() As a two-dimensional barcode, it can store more data than 1D barcodes. ![]() QR Code is widely used in our everyday life. ![]()
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