Simple Object Recognition with the UNIHIKER K10 This project demonstrates that practical object recognition does not always require complex machine learning frameworks. Using only the built-in camera, display, and processing power of the UNIHIKER K10, it is possible to create a simple, fast, and effective object recognition system that can be trained directly by the user. The UNIHIKER K10 is a compact ESP32-S3 based development platform that combines several useful components into a single device: - ESP32-S3 microcontroller - Integrated color display - Built-in camera - Speaker and microphone - Wi-Fi and Bluetooth connectivity - Multiple AI and multimedia capabilities - and Large PSRAM memory for image processing These features make the K10 an excellent platform for experimenting with computer vision, artificial intelligence, robotics, and educational projects. - ESP32-S3 microcontroller - Integrated color display - Built-in camera - Speaker and microphone - Wi-Fi and Bluetooth connectivity - Multiple AI and multimedia capabilities - and Large PSRAM memory for image processing These features make the K10 an excellent platform for experimenting with computer vision, artificial intelligence, robotics, and educational projects. The second experiment used the built-in AI library supplied with the UNIHIKER K10. This demonstrated the ability of the K10 to perform real-time vision tasks using the integrated camera and display. Although these factory AI examples are useful demonstrations, the objective of this project was to create a completely different object recognition method that could be trained directly by the user. I should mention that the Arduino codes for all the examples in this project are given at the end of the text. Now let's move on to the idea from the beginning. The final project introduces a very simple but surprisingly effective object recognition technique. Instead of using neural networks or machine learning models, the system creates a visual signature