diff --git a/README.md b/README.md
index 5d5d5a8..8353005 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,7 @@
## Features
-* Document layout analysis using pixelwise segmentation models with support for 10 distinct segmentation classes:
+* Document layout analysis using pixelwise segmentation models with support for 10 segmentation classes:
* background, [page border](https://ocr-d.de/en/gt-guidelines/trans/lyRand.html), [text region](https://ocr-d.de/en/gt-guidelines/trans/lytextregion.html#textregionen__textregion_), [text line](https://ocr-d.de/en/gt-guidelines/pagexml/pagecontent_xsd_Complex_Type_pc_TextLineType.html), [header](https://ocr-d.de/en/gt-guidelines/trans/lyUeberschrift.html), [image](https://ocr-d.de/en/gt-guidelines/trans/lyBildbereiche.html), [separator](https://ocr-d.de/en/gt-guidelines/trans/lySeparatoren.html), [marginalia](https://ocr-d.de/en/gt-guidelines/trans/lyMarginalie.html), [initial](https://ocr-d.de/en/gt-guidelines/trans/lyInitiale.html), [table](https://ocr-d.de/en/gt-guidelines/trans/lyTabellen.html)
* Textline segmentation to bounding boxes or polygons (contours) including for curved lines and vertical text
* Document image binarization with pixelwise segmentation or hybrid CNN-Transformer models
@@ -81,6 +81,8 @@ Eynollah supports five use cases:
4. [text recognition (OCR)](#ocr), and
5. [reading order detection](#reading-order-detection).
+Some example outputs can be found in [`examples.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/examples.md).
+
### Layout Analysis
The layout analysis module is responsible for detecting layout elements, identifying text lines, and determining reading
@@ -152,16 +154,6 @@ TODO
### OCR
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The OCR module performs text recognition using either a CNN-RNN model or a Transformer model.
The command-line interface for OCR can be called like this:
@@ -176,17 +168,17 @@ eynollah ocr \
The following options can be used to further configure the ocr processing:
-| option | description |
-|-------------------|:------------------------------------------------------------------------------- |
-| `-dib` | directory of bins(files type must be '.png'). Prediction with both RGB and bins. |
-| `-doit` | Directory containing output images rendered with the predicted text |
-| `--model_name` | Specific model file path to use for OCR |
-| `-trocr` | transformer ocr will be applied, otherwise cnn_rnn model |
-| `-etit` | textlines images and text in xml will be exported into output dir (OCR training data) |
-| `-nmtc` | cropped textline images will not be masked with textline contour |
-| `-bs` | ocr inference batch size. Default bs for trocr and cnn_rnn models are 2 and 8 respectively |
-| `-ds_pref` | add an abbrevation of dataset name to generated training data |
-| `-min_conf` | minimum OCR confidence value. OCRs with textline conf lower than this will be ignored |
+| option | description |
+|-------------------|:-------------------------------------------------------------------------------------------|
+| `-dib` | directory of binarized images (file type must be '.png'), prediction with both RGB and bin |
+| `-doit` | directory for output images rendered with the predicted text |
+| `--model_name` | file path to use specific model for OCR |
+| `-trocr` | use transformer ocr model (otherwise cnn_rnn model is used) |
+| `-etit` | export textline images and text in xml to output dir (OCR training data) |
+| `-nmtc` | cropped textline images will not be masked with textline contour |
+| `-bs` | ocr inference batch size. Default batch size is 2 for trocr and 8 for cnn_rnn models |
+| `-ds_pref` | add an abbrevation of dataset name to generated training data |
+| `-min_conf` | minimum OCR confidence value. OCR with textline conf lower than this will be ignored |
### Reading Order Detection