Update README.md

README.md

@@ -25,6 +25,7 @@ This resource includes plain text transcriptions of *The Spiritualist Newspaper*
 The .txt transcription included in this resource was used as a reference string for calculating character error rates against other OCR engines as part of an internally funded National Library of Scotland (NLS) fellowship, within a CERberus local host environment - https://github.com/WHaverals/CERberus. The engines tested included: OCR4ALL, eScriptorium, Transkribus, Tesseract, EasyOCR, Paddle OCR, Surya OCR, GPT 4o, Claude Sonnet 4.5 and Gemini-Pro-3.
 We also include the page images used, with thresholding and additional cropping conducted with R Studio using Image Magick, as well as page-level ALTO XML for interactive image hosting in compliance with IIIF standards (https://iiif.io).
 
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 -- ABOUT THE SPIRITUALIST --
 
 *The Spiritalist Newspaper*, published by E.W. Allen (London), forms a key source for how those interested in physical investigations and spiritual forces communicated with the deceased, argued about criticism and issues, as well as publicised gatherings. The Newspaper followed a wake of spiritualist activity in Britain, directed by earlier 1850s American Transcendentalism and a more genral reaction to scientific naturalism.
@@ -41,6 +42,8 @@ Alongside these two primary reasons, the project also aimed to provide metrics f
 - **Data creation process**: Transformer-based Optical Character Recognition.
 - **Data source**: https://data.nls.uk/data/digitised-collections/spiritualist-newspapers/, 50 pages of the first 1869 edition.Provide a longer summary of what this dataset is. 
 
+![Public trained model in Transkribus environment, used to generate OCR text](Ghostwriter.png)
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 -- ABOUT THE PROJECT --
 
 This transcription output formed part of Dr Joe Nockels's research, 'Recognising Text, Recognising Processes - eXplainable Automatic Text Recognition for Scottish Spiritualist Newspapers', which aimed to establish how far Automatic Text Recognition can coalesce with 'eXplainable' developmental principles, and the extent to which such principles can aid non-technical library users in their understanding of how AI reaches its transcription results. As such, the project's OCR experiments, which ranged from using open-source models to commercial LLMs, occurred alongside a thematic coding of tool's public-facing documentation, including how-to guides, model cards and GitHub README files, as well as other grey literature such as technical papers produced by developmental consortia. This was done through a bespoke web crawler using in-built request delays to ensure server compliance, with eXplainable themes coded via NVIVO (https://lumivero.com/products/nvivo/).