Moved to new structure

_gsoc/image_retrieval.md

@@ -9,15 +9,29 @@ skills:
 forum: https://matrix.to/#/#gsoc-public:edirom.de
 est_project_length: 90 hours
 difficulty: medium
+keywords:
+  - Python
+  - JavaScript
+  - Image Recognition
+  - Image Retrieval
+  - Fingerprinting
+  - Digital Humanities
+  - Open Source
+  - Music
 ---
 
-Scholarly music editions are increasingly being published in a hybrid format. While many practicing musicians prefer printed music in multiple volumes, digital components offer advanced opportunities for scholarly work. A subproject of Edirom is exploring ways to establish a more direct connection between printed and digital materials. The goal is to enable users to scan the page they are currently viewing in a printed volume using a mobile device and immediately access relevant digital enhancements tied to that specific page. In the future, these enhancements could, for example, allow the user to explore a historical handwritten text in augmented reality while sitting in front of a physical book. 
+Scholarly music editions are increasingly being published in a hybrid format. While many practicing musicians prefer printed music in multiple volumes, digital components offer advanced opportunities for scholarly work. A subproject of Edirom is exploring ways to establish a more direct connection between printed and digital materials.
+
+#### Goal
+The goal is to enable users to scan the page they are currently viewing in a printed volume using a mobile device and immediately access relevant digital enhancements tied to that specific page. In the future, these enhancements could, for example, allow the user to explore a historical handwritten text in augmented reality while sitting in front of a physical book. 
 To achieve this, a lightweight, resource-efficient image retrieval algorithm is required—ideally one that can run on a user’s mobile device (using web technologies) or on a remote server. The algorithm should be able to compare a captured image against a database of several thousand pages. While solutions for image retrieval via hashing/fingerprinting already exist, this task poses two primary challenges:
 1. Pages containing text or musical notation often have very similar layouts and may differ only in a few measures or notes, making it easy for a typical low-resolution hashing procedure to miss these subtle distinctions.
 2. Because users take photos under real-world conditions, the images are likely to be imperfect (e.g., poorly lit or misaligned) and may include more than just the page content.
 
-#### Goal
-
 #### Impact
+The project would lay a crucial foundation for integrating the physical and digital aspects of musicological research. Scholarly edition projects, such as the [Korngold Werkausgabe] (https://korngold-werkausgabe.de/en), could leverage this connectivity to develop innovative approaches to working with scholarly editions.
 
-#### Warm-up Tasks
+#### Warm-up Tasks
+1. **Get familiar with the look of scholarly editions of music**, e.g. in the [Digital Mozart Edition](https://dme.mozarteum.at/DME/nma/nmapub_srch.php?l=2) or digitized books like [this one](https://books.google.de/books?id=bD-4DwAAQBAJ).
+2. **Read about modern solutions to image retrieval**, e.g. in articles like [this](https://link.springer.com/article/10.1007/s11042-023-18007-9)
+3. **Familiarize with available libraries** like [blockhash-js](https://github.com/commonsmachinery/blockhash-js), [imghash](https://github.com/pwlmaciejewski/imghash) or [imagehash](https://github.com/JohannesBuchner/imagehash).