![]() Download Xeoma app in App Store (free of charge).Ģ. Install it and enter connection data from the desktop version of Xeoma or Xeoma Cloud account. See Remote Access instructions for Xeoma desktop version.ģ. You can change the camera order in main window by dragging them. Try different values to find the best – to not miss anything (lower than 2000ms) and yet to not send too many pics to PR (higher than 50ms).Configure the system you need exactly - simply and quickly! Remember how you enjoyed playing with your construction set as a kid - combining simple elements and making them grow gradually into something bigger? Even easier than that, now you can create anything with Xeoma's incredible flexibility. Important ANPR should have these settings:ĭetection Interval: higher than the default 50 ms (=0.05 of a second, default timeout). If image crop is needed, you can use the Image Crop module, it is better placed after the camera in the chain.ģ. In Cross-Line place the line with the direction going in which you want to “catch” cars.Ģ. OR instead of the ‘Object Recognizer’ module you could use ‘Object Detector’ module – it reacts to any objects, no matter their type – but we don’t really need it in here. ![]() However, the Cross-Line doesn’t work without a module that tells it about the object type (in the scheme where Cross-Line is placed after the ANPR this role is played by the ANPR) so we’d need an Object Recognizer module where you need to tick object type (car). If it’s critical to filter our direction before sending LPs to Platerecognizer then we’d need to redo the scheme placing the Cross-Line Detector before ANPR. BUT all LPs are sent to Platerecognizer in this setup, and filtering by direction happens after we get the response. If you have the Cross-Line Detector module is after the ANPR – this is great if you need to save only episodes of where LPs are moving in the right direction. In this case the scheme that you have now might be improved in the following ways:ġ. If the goal of the setup is to send to Platerecognizer only the license plates that move in a selected area in the selected direction, right? Please feel free to contact us if you need help with configuration or any other assistance, we’re always happy to help! How do I send less pictures to Platerecognizer? (click to read) ‘localhost’ can be replaced with ip address of the device where Platerecognizer SDK is configured Then you need to specify URL for local SDK in Xeoma: 3. First, you need to configure “Platerecognizer” utility according to this instructionĢ. Platerecognizer utility’s algorithms will be a great tool to improve license plate recognition accuracy thanks to its adjustment to various “real-life” factors, such as sun glare, blurry images, fast vehicles, night-time, and many more.Īnd here is the instruction for local/on-premise SDK installation (on your own computer) click here…ġ. Snapshots/video with decoded license plates can be checked both in your account on and in Xeoma:Īs you can see, configuration of this utility is really easy. ![]() If everything is configured correctly, you’ll see license plate recognition in Xeoma:Ħ. This step describes Cloud API, for local/on-premise SDK installation (on your own computer) please follow this instruction.ĥ. Next you’ll need to open Xeoma (you can download Trial version of Xeoma here) and add ANPR module in your modules chain: After you confirm your email address, you’ll receive API token:ģ. Please visit and sign up to receive API key for testing and using the utility free of charge (or purchase necessary product for your requirements):Ģ. Let’s review step-by-step instruction on how to use Platerecognizer utility (and test it).ġ. ![]() Platerecognizer utility can work both as a cloud software and as a local software (no Internet required) on a variety of hardware. Platerecognizer’s algorithms are able to handle plates that are blurry, dark, angled, with stacked characters, etc. It’s an accurate and fast license plates recognition utility that can work with different license plates. ![]() Xeoma provides multiple ways to enhance license plates recognition by using different utilities such as OpenALPR, iANPR and now – Platerecognizer. Sometimes it’s not that easy to decode a license plate if the image is blurry or somehow distorted. ← Back to Articles Platerecognizer utility for ANPR module in Xeoma ![]()
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