docs: improve README

* Update README.md

* docs: add Docker Compose plugin as requirement

* docs: use docker compose

---------

Co-authored-by: Michelle Weidling <weidling@sub.uni-goettingen.de>

.gitignore

@@ -7,4 +7,5 @@ work/
 workflows/nf-results/*
 workflows/results
 workflows/ocrd-workflows/*.nf
-models
+models
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+.idea
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README.md

 # QuiVer Benchmarks
 
-This repository holds everything you need for automatically executing different OCR-D workflows on images and evaluating the outcomes.
+This repository holds everything you need to automatically execute different OCR-D workflows on images and evaluate the outcomes.
 It creates benchmarks for (your) OCR-D data in a containerized environment.
-You can run QuiVer Benchmarks either locally on your machine or in an automized workflow, e.g. in a CI/CD environment.
+You can run QuiVer Benchmarks either locally on your machine or in an automated workflow, e.g. in a CI/CD environment.
 
 QuiVer Benchmarks is based on `ocrd/all:maximum` and has all OCR-D processors at hand that a workflow might use.
 
-## Prerequisites
+## Requirements
 
 - Docker >= 23.0.0
+- [Docker Compose plugin](https://docs.docker.com/compose/install/linux/#install-using-the-repository)
 
-To speed up QuiVer Benchmarks you can mount already downloaded text recognition models to `/usr/local/share/ocrd-resources/` in `docker-compose.yml` by adding
+To speed up QuiVer Benchmarks you can mount already downloaded text recognition models to `/usr/local/share/ocrd-resources/` in `docker compose.yml` by adding
 
 ```yml
 - path/to/your/models:/usr/local/share/ocrd-resources/
 ```
 
 to the `volumes` section.
-Otherwise the tool will download all `ocrd-tesserocr-recognize` models as well as `ocrd-calamari-recognize qurator-gt4histocr-1.0` on each run.
+Otherwise, the tool will download all `ocrd-tesserocr-recognize` models as well as `ocrd-calamari-recognize qurator-gt4histocr-1.0` on each run.
 
 ## Usage
 
 - clone this repository
-- [customize](#custom-workflows-and-data) QuiVer Benchmarks according to your needs
-- run `docker compose build && docker compose up`
+- (optional) [customize](#custom-workflows-and-data) QuiVer Benchmarks according to your needs
+- run `docker compose up --build`
 - the benchmarks and the evaluation results will be available at `data/workflows.json` on your host system
 
 ## Benchmarks Considered
 
-The relevant benchmarks gathed by QuiVer Benchmarks are defined in [OCR-D's Quality Assurance specification](https://ocr-d.de/en/spec/eval) and comprise
+The relevant benchmarks gathered by QuiVer Benchmarks are defined in [OCR-D's Quality Assurance specification](https://ocr-d.de/en/spec/eval) and comprise
 
 - CER (per page and document wide), incl.
   - median
@@ -41,7 +42,7 @@ The relevant benchmarks gathed by QuiVer Benchmarks are defined in [OCR-D's Qual
 
 ## Custom Workflows and Data
 
-The default behaviour of QuiVer Benchmarks is to collect OCR-D's sample Ground Truth workspaces (currently stored in [quiver-data](https://github.com/OCR-D/quiver-data)), executing the [recommended standard workflows](https://ocr-d.de/en/workflows#recommendations) on these and obtaining the relevant [benchmarks](#benchmarks-considered) for each workflow.
+The default behaviour of QuiVer Benchmarks is to collect OCR-D's sample Ground Truth workspaces (currently stored in [quiver-data](https://github.com/OCR-D/quiver-data)), execute the [recommended standard workflows](https://ocr-d.de/en/workflows#recommendations) on these and obtain the relevant [benchmarks](#benchmarks-considered) for each workflow.
 
 You can, however, customize QuiVer Benchmarks to run your own workflows on the sample workspaces or your own OCR-D workspaces.
 
@@ -49,7 +50,7 @@ You can, however, customize QuiVer Benchmarks to run your own workflows on the s
 
 Add new OCR-D workflows to the directory `workflows/ocrd_worflows` according to the following conventions:
 
-- OCR workflows have to end with `_ocr.txt`, evaluation workflows with `_eval.txt`. The files will be converted by OtoN to Nextflow files after the container has started.
+- OCR workflows have to end with `_ocr.txt`, evaluation workflows with `_eval.txt`. The files will be converted by [OtoN](https://github.com/MehmedGIT/OtoN_Converter) to Nextflow files after the container has started.
 - workflows have to be TXT files
 - all workflows have to use [`ocrd process`](https://ocr-d.de/en/user_guide#ocrd-process)