diff --git a/docs/layoutrecognition.md b/docs/layoutrecognition.md
index 5347ed1..a990ff5 100644
--- a/docs/layoutrecognition.md
+++ b/docs/layoutrecognition.md
@@ -16,7 +16,13 @@ The [```spacy-layout```](https://spacy.io/universe/project/spacy-layout) package
 
 ## Layout recognition with computer vision
 
-When working with tabular data and/or data that were collected in standardised forms (e.g. scientific tables, telegrams, or government reports), you may want to consider using computer vision to identify (handwritten) text within the documents and improve text segmentation. One powerful library for template matching is [OpenCV](https://docs.opencv.org/4.x/d4/dc6/tutorial_py_template_matching.html). In your workflow, you need to list all the documents you want to process and then perform a batch-template matching (e.g. based on a clean template defined with the [GIMP](https://www.gimp.org/) image manipulation software). You can define bespoke text areas in your template and save them to JSON files that can then clearly align found text with metadata (such as archival numbers, dates, names, or places). Then you can use a Python script (set up to be run in a Docker image for better dependency management and security). The actual OCR model that Koen Hufkens used for his transcription of climate data from the Belgian Congo was the French-language model for Tesseract. To learn more about the Tesseract Page Segmentation Modes (PSMs), read [Tesseract Page Segmentation Modes (PSMs) Explained: How to Improve Your OCR Accuracy](https://pyimagesearch.com/2021/11/15/tesseract-page-segmentation-modes-psms-explained-how-to-improve-your-ocr-accuracy/) by Adrian Rosebrock. Koen has documented his process for working with handwritten tabular data in (printed) form on GitHub. Koen Hufkens is active on GitHub as [khufkens](https://github.com/khufkens?tab=repositories).
+When working with tabular data and/or data that were collected in standardised forms (e.g. scientific tables, telegrams, or government reports), you may want to consider using computer vision to identify (handwritten) text within the documents and improve text segmentation. One powerful library for template matching is [OpenCV](https://docs.opencv.org/4.x/d4/dc6/tutorial_py_template_matching.html). In your workflow, you need to list all the documents you want to process and then perform a batch-template matching (e.g. based on a clean template defined with the [GIMP](https://www.gimp.org/) image manipulation software). You can define bespoke text areas in your template and save them to JSON files that can then clearly align found text with metadata (such as archival numbers, dates, names, or places). Then you can use a Python script (set up to be run in a Docker image for better dependency management and security). The actual OCR model that Koen Hufkens used for his transcription of climate data from the Belgian Congo was the French-language model for Tesseract. To learn more about the Tesseract Page Segmentation Modes (PSMs), read [Tesseract Page Segmentation Modes (PSMs) Explained: How to Improve Your OCR Accuracy](https://pyimagesearch.com/2021/11/15/tesseract-page-segmentation-modes-psms-explained-how-to-improve-your-ocr-accuracy/) by Adrian Rosebrock. Koen has documented his process for working with handwritten tabular data originally collected in (pre-printed) form on GitHub. Koen Hufkens is active on GitHub as [khufkens](https://github.com/khufkens?tab=repositories).
+
+## Making use of (recurring) structural features
+
+In layout recognition, it also often helps to think about your data in a very structural, physical sense first. Consider where text on your pages clusters, where white space / negative space defines column formats, and where certain data come up in a fixed order. This can help you improve layout recognition based on truly human perceptions of the data before letting a machine approach it from an algorithmic perspective.
+
+[SEE ARTICLE RECOMMENDED BY KOEN] 
 
 ## Simple batch-processing of images