litteraturlabbet-frontend

Development

The tools were developed by Gothenburg's Research Infrastructure in Digital Humanities (GRIDH) in collaboration with the Literature Bank. Jonathan Westin and Siska Humlesjö have been project managers. Primary developers have been Victor Wåhlstrand Skärström, Aram Karimi, David Alfter, Ashely Green, Jonathan Westin, Kristin Åkerlund and Tristan Bridge. The Text Recycling tool uses Passim, developed by David Smith, to detect similar phrases.

Recommended IDE Setup

VSCode + Volar (and disable Vetur) + TypeScript Vue Plugin (Volar).

Type Support for .vue Imports in TS

TypeScript cannot handle type information for .vue imports by default, so we replace the tsc CLI with vue-tsc for type checking. In editors, we need TypeScript Vue Plugin (Volar) to make the TypeScript language service aware of .vue types.

If the standalone TypeScript plugin doesn't feel fast enough to you, Volar has also implemented a Take Over Mode that is more performant. You can enable it by the following steps:

1. Disable the built-in TypeScript Extension 1) Run Extensions: Show Built-in Extensions from VSCode's command palette 2) Find TypeScript and JavaScript Language Features, right click and select Disable (Workspace)
2. Reload the VSCode window by running Developer: Reload Window from the command palette.

Customize configuration

See Vite Configuration Reference.

Project Setup

npm install

Compile and Hot-Reload for Development

npm run dev

Type-Check, Compile and Minify for Production

npm run build

Lint with ESLint

npm run lint

Images in the gallery

The images shown in the gallery were extracted using a pre-trained Faster R-CNN model with ResNet50 backbone finetuned on a manually labelled subset of the Litterturbanken dataset. The detected images were cropped with a border and exported. The class labels are manually corrected where necessary.

Click on an image in the gallery to see the available metadata about the image and work it belongs to. Below the metadata there is also a gallery of similar images. These similar images were calculated by extracting the image embeddings from a ResNet50 model in Fastai with the TensorBoardCallback for input in Spotify's annoy library to calculate the approximate nearest neighbours with angular distance. Recurring images, those used throughout a work, were excluded using the work id and a match distance threshold.