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Character Recognition (MNIST)

Overview

The Character Recognition application is an Edge AI solution that runs on the MSPM0G5187 microcontroller. It classifies single-digit handwritten character images (0-9) in real-time using the classic LeNet-5 convolutional neural network architecture.

Problem and Solution

  • Handwritten digit recognition is a fundamental task in document processing and automation
  • Manual digit entry is error-prone and time-consuming
  • Traditional template matching approaches struggle with handwriting variability
  • Edge AI enables robust character recognition directly on resource-constrained microcontrollers

Key Performance Targets

  • Real-time digit classification
  • High accuracy on handwritten digits
  • Low memory footprint suitable for MCU deployment
  • Simple UART-based communication with host GUI

System Components

  1. Hardware:

    • MSPM0G5187 microcontroller Link
    • UART connection to host PC

  2. Software:

    • Code Composer Studio 12.x or later
    • MSPM0 SDK 2.08.00 or later

Dataset Information

The example uses the mnist_image_classification dataset based on the classic MNIST handwritten digit dataset:

  • Source: MNIST dataset exported via torchvision
  • Classes: 10 digits (0-9)
  • Image Format: 28x28 grayscale PNG images
  • Download: mnist_classes.zip

For dataset recreation instructions, refer to readme_dataset_creation.md.

Feature Extraction Pipeline

  1. Image Input: 28x28 grayscale image
  2. Grayscale Conversion: Single channel output
  3. Resize: Ensure 28x28 dimensions
  4. Tensor Conversion: Convert to PyTorch tensor
  5. Normalization: Mean = 0.1307, Scale = 0.3081

Model Architecture Options (Available on Tensorlab CLI Tools)

  1. Lenet5 (Default):
    • Classic LeNet-5 CNN architecture
    • ~60,000 parameters
    • Proven architecture for digit recognition

Model Performance

  • Accuracy: ~99%

Training and Deployment Process

NOTE: Running the config yaml handles everything including feature extraction, training, quantization, and compilation.

  1. Training:

    • Use TI Edge AI Studio (GUI) or tinyml-tensorlab (CLI)
    • Batch size: 64, Learning rate: 0.1
    • Training epochs: 14

  2. Quantization:

    • INT8 quantization for reduced model size
    • Maintains accuracy while enabling MCU deployment

  3. Compilation:

    • TI Neural Network Compiler converts trained model
    • Generates model artifacts for CPU inference

How to Run

After completing the repository setup, run the following command from the tinyml-modelzoo directory:

Windows:

  .\run_tinyml_modelzoo.bat examples\MNIST_image_classification\config_image_classification_mnist.yaml

Linux:

  ./run_tinyml_modelzoo.sh examples/MNIST_image_classification/config_image_classification_mnist.yaml

References