Using a TensorFlow Lite Model to Perform Handwritten Digit Recognition on RT1060

Document created by David Piskula Employee on Dec 13, 2019Last modified by David Piskula Employee on Jul 31, 2020
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Convolutional Neural Networks are the most popular NN approach to image recognition. Image recognition can be used for a wide variety of tasks like facial recognition for monitoring and security, car vision for safety and traffic sign recognition or augmented reality. All of these tasks require low latency, great security, and privacy, which can’t be guaranteed when using Cloud-based solutions. NXP eIQ makes it possible to run Deep Neural Network inference directly on an MCU. This enables intelligent, powerful, and affordable edge devices everywhere.


As a case study about CNNs on MCUs, a handwritten digit recognition example was created. It runs on the i.MX RT1060 and uses an LCD touch screen as the input interface. The application can recognize digits drawn with a finger on the LCD.


Handwritten digit recognition is a popular “hello world” project for machine learning. It is usually based on the MNIST dataset, which contains 70000 images of handwritten digits. Many machine learning algorithms and techniques have been benchmarked on this dataset since its creation. Convolutional Neural Networks are among the most successful.


The code is also accompanied by an application note describing how it was created and explaining the technologies it uses. The note talks about the MNIST dataset, TensorFlow, the application’s accuracy and memory footprint and other topics.



Application note URL: (can be found at the documentation page for the i.MX RT1060)


Application code is in the attached zip files: evkmimxrt1060_tensorflow_lite_mnist is the basic application from the left image and evkmimxrt1060_tensorflow_lite_mnist_lock is the extended version from the right image. The applications are provided in the form of MCUXpresso projects and require an existing installation of the i.MX RT1060 SDK with the eIQ component included.


The software for this AN was also ported to CMSIS-NN with a Caffe version of the MNIST model in a follow up AN, which can be found here: 

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