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QR Code Decoder is a powerful online tool that can quickly parse and recognize QR code content. It supports multiple input methods, including direct paste, upload local file, input image URL, Data URL and SVG code. The tool displays complete decoded data and detailed technical information to help you fully understand the content and structure of QR codes.
A: Possible reasons include: 1) Image quality is too low or blurry; 2) QR code is obscured or severely damaged; 3) QR code in the image is too small; 4) Image contrast is insufficient. It's recommended to use clear, high-contrast images and ensure the QR code is fully visible.
A: The tool supports common image formats including PNG, JPEG, WebP, GIF and SVG. PNG format is recommended for best recognition results.
A: No. All QR code decoding processing is done locally in your browser and will not be uploaded to any server, fully protecting your data privacy.
A: Data URL is a way to directly encode image data in a URL, formatted as data:image/png;base64,... This method allows displaying images on web pages without needing separate image files.
A: Yes. The tool uses the jsQR library for decoding, which supports decoding QR codes with logos or certain levels of damage, provided the QR code uses sufficient error correction level (usually Q or H level).
Before decoding QR codes, input images need to be preprocessed, including format conversion, size adjustment, and pixel data extraction. Different input sources (file, URL, Data URL, SVG) require different processing methods. SVG in particular needs to be rendered to Canvas first to extract pixel data.
A key step in QR code recognition is converting the image to a black and white binary image. The jsQR library uses an adaptive threshold algorithm that can handle images with different lighting conditions and contrast. However, in extreme cases (such as severely overexposed or underexposed images), recognition rate will decrease significantly.
QR codes have special position markers at three corners (Position Detection Patterns). The detection algorithm needs to accurately find these three markers in the image and determine the QR code's position, orientation, and size based on their location and size. This is the foundation of the entire recognition process.
jsQR uses the Reed-Solomon error correction algorithm to recover partially damaged or obscured data. Depending on the QR code's error correction level, it can recover 7%-30% of damaged data. This allows successful decoding even when the QR code is partially obscured (such as with an added logo).
When loading images from network URLs, cross-origin issues need to be handled. If the image server doesn't set proper CORS headers, the Canvas will be tainted, preventing pixel data from being read. The solution is to use a proxy or require the image server to support CORS.
A pure JavaScript implementation of a QR code decoding library that can run in browsers without server-side processing. It identifies and decodes QR codes by analyzing image pixel data, supporting various error correction levels and encoding modes.
A graphics drawing API provided by HTML5 that can dynamically generate and manipulate images on web pages. In QR code decoding, Canvas is used to convert images in various formats into pixel data arrays for analysis by the jsQR library.
An object in the Canvas API that represents pixel data, containing the image's width, height, and RGBA format pixel data array. jsQR requires data in this format to perform QR code recognition.
The raw byte data stored in a QR code, in its form before decoding. Depending on the encoding mode, this binary data is interpreted as different types of content such as numbers, text, byte strings, etc.
QR codes use mask patterns to distribute data modules, avoiding large areas of single color and improving recognition reliability. The QR specification defines 8 mask patterns, and the generator selects the optimal pattern.
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