content(cms): update Resource "automatic-text-recognition-atr-video-5…

…-text-recognition-and-post-atr-correction/index"

content/posts/automatic-text-recognition-atr-video-5-text-recognition-and-post-atr-correction/index.mdx

@@ -1,5 +1,5 @@
 ---
-title: "Automatic Text Recognition (ATR) - Part 5: Text Recognition and Post-ATR
+title: "Automatic Text Recognition (ATR) - Step 5: Text Recognition and Post-ATR
   Correction"
 lang: en
 date: 2024-05-10T13:03:58.244Z
@@ -11,19 +11,20 @@ editors:
   - baillot-anne
   - könig-mareike
 tags:
-  - e-heritage
+  - editing-tools
+  - machine-learning
 featuredImage: ""
 abstract: Explore the core concepts of text recognition and model training in
-  our fifth ATR tutorial video. This session breaks down the essentials of
-  creating accurate models, including understanding ground truth data. Perfect
-  for enhancing your ATR skills, the video equips you with the knowledge to
+  our fifth ATR tutorial. This session breaks down the essentials of creating
+  accurate models, including understanding ground truth data. Perfect for
+  enhancing your ATR skills, the tutorial equips you with the knowledge to
   improve text extraction from heritage materials.
 domain: Social Sciences and Humanities
 targetGroup: Domain researchers
-type: video
+type: training-module
 remote:
-  date: 2024-05-06T08:00:00.000Z
-  url: https://youtu.be/P5O8bPR9hXg?si=MwmAmdLVdcQlpb5r
+  date: 2024-05-09T08:00:00.000Z
+  url: https://harmoniseatr.hypotheses.org/226
   publisher: Deutsches Historisches Institut Paris
 licence: ccby-4.0
 toc: false
@@ -32,11 +33,15 @@ uuid: UnTZVwhbc1jkd424wXnxX
 categories:
   - dariah
 ---
-You can watch our video (subtitles available in English, French, and German) or read the accompanying blogpost (also in all three languages): [https://harmoniseatr.hypotheses.org/226](https://harmoniseatr.hypotheses.org/226).
-
 ## Learning Outcomes
 
+After completing this resource, learners will be able to:
+
 - Build and train models to recognise and interpret text within images.
 - Apply concepts of machine learning specific to ATR to improve recognition accuracy.
 - Create ground truth data for training and validating ATR models.
 - Evaluate the effectiveness of different ATR models based on their output quality.
+
+You can read the blogpost (available in English, French, and German), and watch our video (with subtitles in English, French, and German) embedded in the post.
+
+<ExternalResource title="Interested in learning more?" subtitle="Check out &quot;Automatic Text Recognition - Step 5: Text Recognition and Post-ATR Correction" url="https://harmoniseatr.hypotheses.org/226" />