QR Code Reading Benchmark and Comparison
QR Code is widely used in our everyday life. As a two-dimensional barcode, it can store more data than 1D barcodes. It can be captured using a camera and then be decoded using image processing methods.
Reading QR codes in the real world is a challenging job. For example, the following QR code is damaged and wrinkled, which is difficult to read.
There are many open-source and commercial libraries or SDKs which can read QR codes. A benchmark is needed to evaluate which one is more robust or suitable for a specific use case.
There aren’t many QR codes reading benchmarks. A comprehensive one is made by the author of the open-source computer vision library Boofcv. The author collected a dataset of QR code images and put them into categories. A performance test is then made on the dataset to evaluate 5 open-source libraries.
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. In this article, we are going to do a QR code reading benchmark using this dataset.
This article is Part 1 in a 5-Part Series.
Performance Test Tool
In order to run the benchmark, a performance test tool is written.
The tool uses Python to run the test and provides a web interface. The Flask web framework is used.
You can find its code here.
It is going to run on a PC device with an Intel i5-10400 CPU and 16GB memory.
Evaluated Libraries
We are going to evaluate 7 libraries and SDKs:
- Dynamsoft Barcode Reader (DBR, version: 8.6, commercial)
- Commercial SDK A (version: Java 13.5, commercial)
- Commercial SDK B (version: Windows 5.19.3.10, commercial)
- BoofCV (version: pyboof 0.36.1, open-source)
- Zxing (version: 3.4.1, open-source)
- Zbar (version: pyzbar 0.1.8, open-source)
- WeChat QR code detector in OpenCV (version: OpenCV 4.5.3, open-source)
Evaluation Metrics
The performance is evaluated by reading rate and runtime.
We can get the reading rate by dividing detected QR codes by total QR codes in images.
Reading rate = Detected QR codes / Total QR codes
How to infer that a QR code is detected? If the value of IoU (Intersection over Union) between the detected QR code’s polygon and the ground truth’s is over 0, then the QR code is considered detected.
Here is the simplified code:
detected_ground_truth = []
for ground_truth in ground_truth_list:
detected = False
for detected_result in detected_result_list:
if iou<=0.0: #if not overlapped
continue
else:
detected = True
break
if detected:
detected_ground_truth.append(ground_truth)
The IoU is calculated using the following code:
from shapely.geometry import Polygon
def calculate_polygon_iou(points1,points2):
poly1 = Polygon(points1)
poly2 = Polygon(points2)
# ratio of intersection area to union area
iou = poly1.intersection(poly2).area/poly1.union(poly2).area
return iou
# convert localization results of x1, y1...x4, y4 to a list of points
def points_of_one_detection_result(result):
p = [(float(result["x1"]), float(result["y1"])),
(float(result["x2"]), float(result["y2"])),
(float(result["x3"]), float(result["y3"])),
(float(result["x4"]), float(result["y4"]))]
return p
The QR code’s text result is not considered since the libraries all have 100% precision.
Note:
Zxing only returns the three points of the position patterns in a QR code. Postprocessing is done to convert the three points to a polygon. Since the IoU threshold is low, this works okay.
The runtime is calculated using the following code.
start_time = time.time()
result_dict = self.reader.decode_file(os.path.join(self.img_folder,filename))
end_time = time.time()
elapsedTime = int((end_time - start_time) * 1000)
Detection Results
Reading rate in percent:
| Categories | Dynamsoft | Commercial SDK A | Commercial SDK B | Zxing | Zbar | BoofCV | OpenCV Wechat |
|---|---|---|---|---|---|---|---|
| blurred | 69.23 | 38.46 | 26.15 | 23.08 | 38.46 | 40.00 | 55.38 |
| bright_spots | 38.14 | 30.93 | 6.19 | 21.65 | 19.59 | 23.71 | 30.93 |
| brightness | 81.18 | 52.94 | 32.94 | 54.12 | 51.76 | 78.82 | 68.24 |
| close | 92.50 | 25.00 | 35.00 | 5.00 | 12.50 | 77.50 | 65.00 |
| curved | 73.33 | 38.33 | 33.33 | 30.00 | 35.00 | 50.00 | 43.33 |
| damaged * | 53.49 | 27.91 | 23.26 | 20.93 | 25.58 | 16.28 | 48.84 |
| glare | 86.79 | 20.75 | 22.64 | 20.75 | 35.85 | 32.08 | 64.15 |
| high_version * | 97.30 | 35.14 | 59.46 | 5.41 | 27.03 | 40.54 | 18.92 |
| lots * | 60.48 | 97.86 | 1.90 | 82.86 | 18.10 | 99.76 | 0.00 |
| monitor | 100 | 5.88 | 5.88 | 0.00 | 0.00 | 64.71 | 94.12 |
| nominal | 91.03 | 62.82 | 51.28 | 51.28 | 66.67 | 89.74 | 80.77 |
| noncompliant * | 88.46 | 3.85 | 15.38 | 19.23 | 50.00 | 3.85 | 92.31 |
| pathological * | 100 | 78.26 | 78.26 | 34.78 | 65.22 | 43.48 | 91.30 |
| perspective | 68.57 | 34.29 | 51.43 | 37.14 | 42.86 | 82.86 | 42.86 |
| rotations | 99.25 | 67.67 | 63.91 | 42.86 | 49.62 | 96.99 | 82.71 |
| shadows | 100 | 95.00 | 80.00 | 65.00 | 90.00 | 85.00 | 85.00 |
| total average | 81.23 | 44.69 | 36.69 | 32.13 | 39.27 | 57.83 | 60.24 |
- Damaged QR codes are the ones with scratches, dents, etc.
- Pathological QR codes have the markers intentionally corrupted. The markers are designed for detection of QR Codes.
- The lots category has multiple QR Codes in one image.
- The QR Code symbology ranges from Version 1 to Version 40. The Version Information represents the number of cells (per side) making up the code. More on the QR Code versions.
- Non-compliant QR Codes have customized designs, for example, colors, or a logo at the center.
Dynamsoft Barcode Reader Wins by a Large Margin
We can see that the Dynamsoft Barcode Reader ranks 1st in most of the categories. 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%.
It ranks 2nd in the noncompliant and perspective categories and 4th in the lots category.
Note: The reading rates of some categories are not very high, because the QR codes are in bad conditions and not readable.
ZXing vs. ZBar vs. BoofCV vs. OpenCV WeChat
If open source is a must for your project, it’s a safer choice to go with BoofCV or OpenCV WeChat. These two are based on computer vision and are actively maintained. This could be why they outperform ZXing and ZBar in some challenging situations.
- OpenCV WeChat is robust. It has the highest success rate for damaged and pathological QR Codes.
- OpenCV WeChat also excels for non-compliant QR Codes, which is not surprising, as QR codes with colors, or a logo at the center, or other customized designs are ubiquitous in WeChat usage.
- If we cast aside the noncompliant and pathological codes, then BoofCV is a good free solution, especially when it comes to the “lots” category.
Runtime Results
Runtime per image (in milliseconds):
| Engine | Result |
|---|---|
| DBR | 141.10 |
| Commercial SDK A | 314.04 |
| Commercial SDK B | 48.31 |
| Zxing | 181.68 |
| Zbar | 199.26 |
| BoofCV | 205.48 |
| OpenCV Wechat | 175.06 |
Runtime per image by categories (in milliseconds):
| Categories | Dynamsoft | Commercial SDK A | Commercial SDK B | Zxing | Zbar | BoofCV | OpenCV Wechat |
|---|---|---|---|---|---|---|---|
| blurred | 298.00 | 335.16 | 39.67 | 206.89 | 177.58 | 138.16 | 125.31 |
| bright_spots | 252.84 | 625.47 | 47.56 | 311.59 | 319.81 | 221.81 | 112.25 |
| brightness | 192.75 | 600.14 | 47.86 | 320.32 | 349.79 | 273.43 | 134.79 |
| close | 169.40 | 607.27 | 80.08 | 295.18 | 381.65 | 150.70 | 676.00 |
| curved | 188.76 | 300.82 | 43.40 | 173.44 | 220.28 | 103.42 | 129.54 |
| damaged | 200.03 | 205.49 | 42.05 | 84.41 | 96.76 | 46.14 | 138.35 |
| glare | 104.66 | 217.60 | 45.40 | 130.32 | 157.30 | 70.80 | 95.38 |
| high_version | 106.06 | 349.21 | 79.88 | 294.97 | 141.70 | 268.36 | 566.03 |
| lots | 275.43 | 851.14 | 110.14 | 1073.00 | 1477.57 | 6149.00 | 139.57 |
| monitor | 176.59 | 812.53 | 105.88 | 233.29 | 515.47 | 142.47 | 422.76 |
| nominal | 74.05 | 203.65 | 32.12 | 141.49 | 111.68 | 109.49 | 54.54 |
| noncompliant | 58.88 | 81.63 | 34.44 | 47.81 | 57.06 | 31.94 | 43.31 |
| pathological | 8.52 | 4.09 | 65.87 | 3.00 | 1.83 | 14.61 | 19.39 |
| perspective | 25.74 | 46.34 | 14.91 | 27.66 | 24.31 | 26.74 | 24.60 |
| rotations | 72.11 | 173.84 | 35.80 | 106.84 | 134.66 | 178.45 | 86.91 |
| shadows | 102.21 | 243.50 | 42.29 | 150.00 | 163.29 | 117.50 | 86.36 |
| total average | 144.13 | 353.62 | 54.21 | 225.01 | 270.67 | 502.69 | 178.44 |
Because Boofcv spent a lot of time on decoding the lots category, we separated it from the main chart.
Although the commercial SDK B has low reading rate on the dataset, it is the fastest. The Dynamsoft Barcode Reader ranks second on average.
Update Runtime Settings to Improve the Results
The benchmark is done without updating the SDKs’ default settings. We can update the runtime settings to improve the reading accuracy or speed.
Among the SDKs, only the Dynamsoft Barcode Reader provides rich parameters that users can customize and optimize for different usage scenarios for the best scanning performance.
The runtime settings of DBR can be represented and modified using a JSON template. The following is the default template used in DBR Python.
{
"ImageParameter" : {
"BarcodeColourModes" : [
{
"LibraryFileName" : "",
"LibraryParameters" : "",
"LightReflection" : 1,
"Mode" : "BICM_DARK_ON_LIGHT"
}
],
"BarcodeComplementModes" : [
{
"Mode" : "BCM_SKIP"
}
],
"BarcodeFormatIds" : [ "BF_ALL" ],
"BarcodeFormatIds_2" : null,
"BinarizationModes" : [
{
"BlockSizeX" : 0,
"BlockSizeY" : 0,
"EnableFillBinaryVacancy" : 1,
"ImagePreprocessingModesIndex" : -1,
"LibraryFileName" : "",
"LibraryParameters" : "",
"Mode" : "BM_LOCAL_BLOCK",
"ThresholdCompensation" : 10
}
],
"ColourClusteringModes" : [
{
"Mode" : "CCM_SKIP"
}
],
"ColourConversionModes" : [
{
"BlueChannelWeight" : -1,
"GreenChannelWeight" : -1,
"LibraryFileName" : "",
"LibraryParameters" : "",
"Mode" : "CICM_GENERAL",
"RedChannelWeight" : -1
}
],
"DPMCodeReadingModes" : [
{
"Mode" : "DPMCRM_SKIP"
}
],
"DeblurLevel" : 9,
"DeblurModes" : null,
"DeformationResistingModes" : [
{
"Mode" : "DRM_SKIP"
}
],
"Description" : "",
"ExpectedBarcodesCount" : 0,
"FormatSpecificationNameArray" : null,
"GrayscaleTransformationModes" : [
{
"LibraryFileName" : "",
"LibraryParameters" : "",
"Mode" : "GTM_ORIGINAL"
}
],
"ImagePreprocessingModes" : [
{
"LibraryFileName" : "",
"LibraryParameters" : "",
"Mode" : "IPM_GENERAL"
}
],
"IntermediateResultSavingMode" : {
"Mode" : "IRSM_MEMORY"
},
"IntermediateResultTypes" : [ "IRT_NO_RESULT" ],
"LocalizationModes" : [
{
"LibraryFileName" : "",
"LibraryParameters" : "",
"Mode" : "LM_CONNECTED_BLOCKS"
},
{
"LibraryFileName" : "",
"LibraryParameters" : "",
"Mode" : "LM_SCAN_DIRECTLY",
"ScanDirection" : 0,
"ScanStride" : 0
},
{
"LibraryFileName" : "",
"LibraryParameters" : "",
"Mode" : "LM_STATISTICS"
},
{
"LibraryFileName" : "",
"LibraryParameters" : "",
"Mode" : "LM_LINES"
}
],
"MaxAlgorithmThreadCount" : 4,
"Name" : "CurrentRuntimeSettings",
"PDFRasterDPI" : 300,
"PDFReadingMode" : {
"Mode" : "PDFRM_AUTO"
},
"Pages" : "",
"RegionDefinitionNameArray" : null,
"RegionPredetectionModes" : [
{
"LibraryFileName" : "",
"LibraryParameters" : "",
"Mode" : "RPM_GENERAL"
}
],
"ResultCoordinateType" : "RCT_PIXEL",
"ReturnBarcodeZoneClarity" : 0,
"ScaleDownThreshold" : 2300,
"ScaleUpModes" : [
{
"Mode" : "SUM_AUTO"
}
],
"TerminatePhase" : "TP_BARCODE_RECOGNIZED",
"TextAssistedCorrectionMode" : {
"BottomTextPercentageSize" : 0,
"LeftTextPercentageSize" : 0,
"LibraryFileName" : "",
"LibraryParameters" : "",
"Mode" : "TACM_VERIFYING",
"RightTextPercentageSize" : 0,
"TopTextPercentageSize" : 0
},
"TextFilterModes" : [
{
"LibraryFileName" : "",
"LibraryParameters" : "",
"MinImageDimension" : 65536,
"Mode" : "TFM_GENERAL_CONTOUR",
"Sensitivity" : 0
}
],
"TextResultOrderModes" : [
{
"Mode" : "TROM_CONFIDENCE"
},
{
"Mode" : "TROM_POSITION"
},
{
"Mode" : "TROM_FORMAT"
}
],
"TextureDetectionModes" : [
{
"LibraryFileName" : "",
"LibraryParameters" : "",
"Mode" : "TDM_GENERAL_WIDTH_CONCENTRATION",
"Sensitivity" : 5
}
],
"Timeout" : 10000
},
"Version" : "3.0"
}
You can learn more about the parameters of DBR here.
Modify the Template to Decode the Lots Category
In the benchmark, there are several files in the lots category that DBR cannot detect all the QR codes.
The reason is that the QR codes are relatively small and the image has a large resolution (3024x4032) while in DBR’s template, there is a ScaleDownThreshold parameter which is set to 2300 so that the image will be scaled down for a better decoding speed.
If we set the ScaleDownThreshold to 99999, we can decode all the QR codes.
The reading rate for the lots category can increase from 60.48 to 100 using the modified template.
Modify the Template to Decode the Blurred Category
DBR comes with many image processing algorithms.
For blurred QR codes as below, we can use the GRAY_SMOOTH image processing method before deblurring to decode it.
"ImagePreprocessingModes" : [
{
"LibraryFileName": "",
"LibraryParameters": "",
"Mode": "IPM_GRAY_SMOOTH",
"SmoothBlockSizeX": 3,
"SmoothBlockSizeY": 3
}
]
But if the QR codes are blurred too much, we cannot do much about this. DBR can only decode one extra image after using the modified template in the blurred category. We should try to capture clear QR code images in the first place. SDKs like the Dynamsoft Camera Enhancer can help on this aspect.
Conclusion
The Dynamsoft Barcode Reader has the best QR code reading accuracy on the test set. Its speed is good and has rich runtime settings to adapt to different scenarios. If conditions allow, it is better to choose DBR as the barcode reading SDK.