In this lesson, you’ll learn computer vision and machine learning principles for object recognition, and how to apply these principles using Python to recognize and classify smiling faces in historical photographs.
This tutorial explores where and how to find, create, and collect images of textual material, a crucial initial step in any process using Automatic Text Recognition (ATR).
Word embeddings allow you to analyze the usage of different terms in a corpus of texts by capturing information about their contextual usage. Through a primarily theoretical lens, this lesson will teach you how to prepare a corpus and train a word embedding model. You will explore how word vectors work, how to interpret them, and how to answer humanities research questions using them.
Kick off your journey into Automatic Text Recognition (ATR) with our introductory tutorial video. This is the first video of a tutorial series dedicated to extracting full text from scanned images.
This lesson uses word embeddings and clustering algorithms in Python to identify groups of similar documents in a corpus of approximately 9,000 academic abstracts. It will teach you the basics of dimensionality reduction for extracting structure from a large corpus and how to evaluate your results.
This lesson provides a beginner-friendly introduction to convolutional neural networks (CNNs) for image classification. The tutorial provides a conceptual understanding of how neural networks work by using Google’s Teachable Machine to train a model on paintings from the ArtUK database. This lesson also demonstrates how to use Javascript to embed the model in a live website.
In this lesson, you will learn how to apply a Generative Pre-trained Transformer language model to a large-scale corpus so that you can locate broad themes and trends within written text.
This resource from the CLS INFRA project offers an introduction to several research areas and issues that are prominent withinComputational Literary Studies (CLS), including authorship attribution, literary history, literary genre, gender in literature, and canonicity/prestige, as well as to several key methodological concerns that are of importance when performing research in CLS.
This is the first of a two-part lesson introducing deep learning based computer vision methods for humanities research. Using a dataset of historical newspaper advertisements and the fastai Python library, the lesson walks through the pipeline of training a computer vision model to perform image classification.
