diff --git a/README.md b/README.md
index f557c30..0239c35 100644
--- a/README.md
+++ b/README.md
@@ -1,24 +1,39 @@
 # MATS — Morphometric Analysis Toolbox
 
 Measure leaf **area, length, and width** in real-world units from a photo of
-leaves laid on a printed calibration template. MATS finds four fiducial markers
-with RF-DETR, corrects perspective, segments each leaf with a fast Otsu
-threshold by default (or the heavier BiRefNet model for tougher backgrounds),
-and writes a measurements CSV.
+leaves laid on a printed calibration template.
 
-Pipeline in one line: **detect markers → perspective-correct → segment leaf →
-measure → CSV**.
+MATS has four main steps:
+1. Locate four fiducial markers using an RF-DETR detection model.
+2. Applies a transform to undo any perspective distortion.
+3. Segments each leaf, using either a fast Otsu threshold (default option) or a BiRefNet segmentation model for tougher backgrounds.
+4. Writes out a CSV of the measurements.
 
 > Companion code for the manuscript (target journal: *Plant Phenomics*).
 > BiRefNet is optional and runs entirely from a locally installed checkpoint (see [Model weights](#model-weights)).
 > For USDA users the model weights are hosted on Agdatacommons and the pipeline is available on SciNET
 
+## Table of Contents  
+
+1. [Installation](#installation)  
+3. [Model weights](#model-weights)
+4. 2. [Setup??](#)
+
+5. [Outputs](#outputs)
+6. [Running on a compute cluster](#running-on-a-compute-cluster)
+7. [How it works](#how-it-works)
+8. [Troubleshooting](#troubleshooting)
+9. [Citing](#citing)
+10. [License](#license)
+
 ---
 
-## Install
 
-MATS needs only Python ≥ 3.9 — QR codes are decoded with OpenCV, so the default
-install pulls everything from wheels with **no system libraries and no conda
+
+## Installation
+
+MATS requires only Python ≥ 3.9; the recommended `pip` installation method
+pulls all required packages from wheels with **no system libraries and no conda
 required**.
 
 **pip (recommended):**
@@ -29,8 +44,9 @@ cd Morphometric-Analysis-Toolbox-for-Segmentation
 pip install -e ".[app]"                  # ".[app]" adds the Streamlit GUI
 ```
 
-**Robust QR reading (optional).** OpenCV decodes clean codes reliably. For
-tougher photos (glare, skew, blur), add the `pyzbar` + `qreader` fallbacks:
+**Enhanced QR reading (optional).** OpenCV reads QR codes well when they are oriented correctly and clearly
+visible. For images with any issues affecting the QR codes (glare, skew, blur) you can add the `pyzbar` + `qreader` fallbacks.
+`pyzbar` requires the system library `zbar`:
 
 ```bash
 pip install -e ".[app,qr]"
@@ -49,13 +65,36 @@ If a code can't be read, the pipeline continues — pass the finished sheet size
 with `--sheet-dimensions` (for example, `--sheet-dimensions 12x12in`), so
 enhanced QR is a convenience rather than a requirement.
 
-Then fetch the model weights once and confirm the environment:
+Finally, fetch the model weights and confirm the environment:
 
 ```bash
 mats fetch-weights      # fetches the ~134 MB RF-DETR checkpoint (mandatory, default)
 mats fetch-weights --only birefnet --source lfs # optional: explicitly fetch the ~2.65 GB BiRefNet checkpoint
 mats doctor             # checks weights, GPU/CPU device, QR backends
 ```
+---
+
+## Model weights
+
+The checkpoints for both the marker detection model and the leaf segmentation model are located in this repository:
+
+| Model | File | Size |
+|---|---|---|
+| RF-DETR marker detector | `rf_detr_marker.pth` | ~134 MB |
+| BiRefNet leaf segmenter | `birefnet_leaf.pth` | ~2.65 GB |
+
+By default, only the RF-DETR model checkpoint will be downloaded.
+The BiRefNet checkpoint is LFS-tracked but excluded from the default clone, so it is
+downloaded only through an explicit action:
+
+- **Otsu (default)** — needs no BiRefNet checkpoint and never downloads one.
+- **BiRefNet (optional)** — fetch explicitly with
+  `mats fetch-weights --only birefnet --source lfs`, or use the setup page.
+- **Shared filesystem** — set `MATS_WEIGHTS_DIR` (e.g. a SCINet `/project` path)
+  to read weights in place with no per-user copy.
+
+Full details and checksums: [docs/weights.md](docs/weights.md).
+
 
 ### Why the first installation is lightweight
 
@@ -86,7 +125,7 @@ a run.
 
 ---
 
-## Choose your path
+## Running MATS
 
 - **I want to click buttons →** [Using the app](#using-the-app)
 - **I want to script it →** [Using the command line](#using-the-command-line)
@@ -95,21 +134,22 @@ Both run the exact same pipeline and produce the same measurements.
 
 ---
 
-## Using the app
+### Using the app
+
+MATS comes with a point-and-click user interface. To open it, simply run:
 
 ```bash
 mats app
 ```
 
-This opens the Streamlit GUI in your browser. From there:
+This will open the Streamlit app locally in your web browser. From there:
 
 1. **Pick images** — a local folder, or drag-and-drop uploads.
-2. **Set the scale** — enter the finished printed sheet's width, height, and
-   unit (for example, `12 x 12 in`). MATS applies the Template Creator margin
-   rules and derives the marker-centre calibration area automatically. Or tick
-   **Variable dimensions, read QR code** to read each image's calibration.
-3. **Choose segmentation** — Otsu threshold (fast, default) or BiRefNet (accurate when its optional local checkpoint is installed).
-4. **Choose workers** — the app detects the CPUs assigned to it. One worker uses
+2. **Set the scale** — enter the printed sheet's width, height, and unit (e.g.
+   `10.5 x 9.5 in`), or tick **Variable dimensions, read QR code** to read it
+   from each image's template QR code automatically.
+3. **Choose segmentation** — Otsu threshold (fast, default) or BiRefNet (accurate, must have local model checkpoint installed).
+4. **Choose workers** — the app detects the number of CPUs available to it. One worker uses
    CUDA/MPS when available; two or more workers use parallel CPU processing and
    disable CUDA/MPS for that run. A colored warning light shows CPU allocation;
    counts above 75% require a one-run break-glass acknowledgement.
@@ -129,7 +169,7 @@ photograph it flat. See [docs/templates.md](docs/templates.md).
 
 ---
 
-## Using the command line
+### Using the command line
 
 ```bash
 mats run -i ./images -o ./out -r results.csv --sheet-dimensions 12x12in
@@ -198,29 +238,11 @@ A `leaf_morpho_failures.csv` records per-image warnings and failures.
 
 ---
 
-## Model weights
-
-The checkpoints are tracked in this repository with Git LFS:
-
-| Model | File | Size |
-|---|---|---|
-| RF-DETR marker detector | `rf_detr_marker.pth` | ~134 MB |
-| BiRefNet leaf segmenter | `birefnet_leaf.pth` | ~2.65 GB |
-
-RF-DETR is available in a normal checkout. BiRefNet is LFS-tracked but excluded
-from the default clone, so it is downloaded only through an explicit action:
-
-- **Otsu (default)** — needs no BiRefNet checkpoint and never downloads one.
-- **BiRefNet (optional)** — fetch explicitly with
-  `mats fetch-weights --only birefnet --source lfs`, or use the setup page.
-- **Shared filesystem** — set `MATS_WEIGHTS_DIR` (e.g. a SCINet `/project` path)
-  to read weights in place with no per-user copy.
 
-Full detail and checksums: [docs/weights.md](docs/weights.md).
 
 ---
 
-## On a cluster (HPC / Open OnDemand)
+## Running on a compute cluster
 
 An Open OnDemand Batch Connect app that serves the GUI on a compute node is in
 [deploy/ondemand/mats/](deploy/ondemand/mats/). See its README and