Step-By-Step Handwriting Words Recognition With PyTorch
In this tutorial, we will extend the previous tutorial to build a custom PyTorch model using the IAM Dataset for recognizing handwritten text. This dataset is commonly used as a benchmark for OCR systems and can provide a valuable foundation for constructing your own OCR system. We will be using several machine learning libraries and techniques to preprocess the data, augment it, and train a deep learning model.
During this tutorial, we will cover the following:
An overview of the IAM Dataset and handwritten text recognition;
Code walkthrough for importing required modules and libraries;
Downloading and extracting the dataset using the download_and_unzip function;
Preprocessing the dataset, including data parsing, vocab set creation, and maximum label length;
Data augmentation techniques to improve model performance;
A deep dive into PyTorch model training with custom CTC loss function and callbacks;
Evaluation metrics like CER and WER to monitor training progress;
Saving and exporting the trained PyTorch model in ONNX format.
By the end of this tutorial, you will have a good understanding of how to train a custom PyTorch model for recognizing handwritten text using the IAM Dataset. Join me in this exciting journey of handwriting recognition with PyTorch!
Text Version Tutorial: https://pylessons.com/pytorch-wrapper
GitHub: https://github.com/pythonlessons/mltu...
pypi: https://pypi.org/project/mltu/
#machinelearning #python #pytorch #ocr #tensorflow