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We implemented `ANPR/ALPR(Automatic Number/License Plate Recognition)` engine with unmatched accuracy and precision by applying `SOTA(State-of-the-art)` deep learning techniques in this repository.
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This repository demonstrates `ANPR/ALPR` model inference in `Linux` server.
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KBY-AI's `LPR` solutions utilizes artificial intelligence and machine learning to greatly surpass legacy solutions. Now, in real-time, users can receive a vehicle's plate number.
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This repository demonstrates `ANPR/ALPR(Automatic Number/License Plate Recognition)` SDK with unmatched accuracy and precision by applying `SOTA(State-of-the-art)` deep learning techniques. </br>
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`KBY-AI`'s `LPR` solutions utilizes artificial intelligence and machine learning to greatly surpass legacy solutions. Now, in real-time, users can receive a vehicle's plate number through `API`.
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> We can customize the `SDK` to align with customer's specific requirements.
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>
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The `ALPR` system consists of the following steps:
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- Vehicle image capture
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- Preprocessing
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The `ALPR` system works in these strides, the initial step is the location of the vehicle and capturing a vehicle image of front or back perspective of the vehicle, the second step is the localization of Number Plate and then extraction of vehicle Number Plate is an image. The final stride uses image segmentation strategy, for the segmentation a few techniques neural network, mathematical morphology, color analysis and histogram analysis. Segmentation is for individual character recognition. Optical Character Recognition (OCR) is one of the strategies to perceive the every character with the assistance of database stored for separate alphanumeric character.
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## Online Test Demo
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To try `KBY-AI``ALPR` online, please visit [here](https://web.kby-ai.com/)
This project demonstrates `KBY-AI`'s `Palmprint Recognition Server SDK`, which requires a license per machine.</br>
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- The code below shows how to use the license: https://github.com/kby-ai/Palmprint-Recognition-Docker/blob/290f714ca49496164f0586f277b6104bfd164ad7/app.py#L21-L31
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- To request the license, please provide us with the `machine code` obtained from the `getMachineCode` function.</br>
roi = mat_to_bytes(get_roi(img, hand_type, x1, y1, x2, y2))
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```
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*`hand_type`: it indicates hand type value, `0` value: `left hand`, `1` value: `right hand`.
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*`x1`, `y1`, `x2`, `y2`: hand landmark points to get `ROI` image.
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*`roi`: hand `ROI(Region Of Interest)` image to get palm feature.
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- Create Feature
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`encode_using_bytes`functionreturns palmprint feature against `ROI` data.</br>
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```python
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palmprint = encoder.encode_using_bytes(roi)
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```
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*`roi`: hand `ROI(Region Of Interest)` image to get palm feature.
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*`palmprint`: palmprint feature calculated from hand `ROI` data.
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- Similiarity
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The `compare_to`functiontakes two palmprint `feature`s as a parameter and returns `score` value to determine whether 2 input hands are from the same or different.
You can visit our YouTube video for`ANPR/ALPR` model's performance [here](https://www.youtube.com/watch?v=sLBYxgMdXlA) to see how well our demo app works.</br></br>
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