Description

Real time digit recognition using tensorflow

 Handwriting digit recognition application is used in different tasks of our real-life time purposes. Precisely, it is used in vehicle number plate detection, banks for reading checks, post offices for sorting letter, and many other related tasks.The digit recognition is performed by a TensorFlow Lite model, with an architecture similar to LeNet-5 (LeCun, LeNet-5, convolutional neural networks), which was converted from the TensorFlow implementation.Handwritten digit recognition with models trained on the MNIST dataset is a popular “Hello World” project for deep learning as it is simple to build a network that achieves over 90 % accuracy for it.To enable TensorFlow on mobile and embedded devices, Google developed the TensorFlow Lite framework. It gives these computationally restricted devices the ability to run inference on pre-trained TensorFlow models that were converted to TensorFlow Lite. These converted models cannot be trained any further but can be optimized through techniques like quantization and pruning. However, TensorFlow Lite does not support all the original TensorFlow’s operations and developers must keep that in mind when creating models.