Wikimedia Foundation

Mentors: Student: Abhinavmohandas

The proposal is on the project Montage which is the most used photo evaluation tool under WLM ( Wiki Loves Monuments ). The plan is mainly about adding required features and infrastructural upgrades like multi worker system or redis on the project as the amount of entries ( images ) it handles is very high and the current structure barely supports it.

Mentors: Student: Gautham Mohanraj

This project introduces Conjugate, an Android and iOS open-source mobile application that provides a privacy-respecting verb conjugation tool powered by Wikidata. It aims to offer a free and accessible alternative to existing solutions that are often paid or ad-driven. The application will integrate prebuilt SQLite databases to enable fast verb search, real-time autocomplete, and structured conjugation views. Development will reuse existing Scribe-Android components and adopt SwiftUI for iOS, with a focus on modular architecture and performance. Key deliverables include fully functional apps on both platforms, efficient multilingual search, a dynamic conjugation interface, and a well-tested, maintainable codebase that expands the Scribe ecosystem.

Mentors: Student: Pius Osuji

CampWiz NXT is the jury management platform used during Wiki Loves contests to evaluate media submissions, images, audio, and video, uploaded to Wikimedia Commons. It provides a structured environment for jurors and organisers to assess, score, and process contest entries at scale. The current implementation uses a dual-server architecture: a Next.js frontend and a Golang backend. While functional, this setup introduces real costs. There is server component complexity, a persistent Node.js process required for deployment, and an interface that places more cognitive burden on jurors than the work itself demands. These are solvable problems, and solving them is what this project is about. The goal is to migrate the frontend from Next.js to a pure React application built as a static output with Vite. This is not a surface-level reskin. The migration removes the Node.js server dependency entirely, allowing the frontend to be served over standard HTTP, making deployment simpler, cheaper, and more maintainable. The Golang backend remains unchanged; only the frontend layer is being replaced. Alongside the technical migration, the interface will be redesigned with a deliberate focus on clarity and minimalism. Jurors should be able to open the tool and immediately focus on reviewing submissions, not on navigating a cluttered interface. Non-essential actions will be removed from the juror viewport, decision controls will be streamlined, and keyboard accessibility will be enforced throughout. The organiser dashboard will receive the same treatment: clean workflows for contest setup, juror assignment, and results management, without requiring backend access for routine configuration. The result will be a frontend that is easier to deploy, easier to maintain, and noticeably better to use.

Mentors: Student: Egezort

Wikidata currently lacks a strict, universally applied method to differentiate between instances, first-order classes, and metaclasses. This structural ambiguity actively degrades the reliability of the knowledge graph's ontology. This project proposes a web-based, gamified crowdsourcing tool designed to systematically categorize Wikidata entities and clean up its class order. The platform will present users with ambiguous entities and ask them to classify the item as an "individual item," "first-order class," or "metaclass" based on available statements (e.g., P279 and P31 links). To guarantee strict data correctness, the tool will implement a multi-tiered user hierarchy (Newbies, Players, and Experts) and rely on a consensus mechanism. Edge cases and disputed classifications are automatically escalated to an expert backlog. To drive user retention and data quality, the tool will feature gamified mechanics, rewarding players with points for matching the correct consensus and offering high-value rewards for successfully overturning incorrect consensus through expert review.

Mentors: Student: Rishan

This project is proposed to 1. Prevent the loss in details happening when the user edits, uses the crop/rotate feature when uploading images through the Wikimedia Commons app by replacing LLJTran with JPEGTran which handles details gracefully. 2. Implement Lossless Block-Level Blur The Blur feature at present makes quality loss of image in the process. This is solved by recompression to only relevant blur required pixel blocks then untouched areas wont have loss in details. 3. Auto-detect and blur faces & car number plates. When the user uploads images there is less privacy for random people and car number plates captured by user in the photo use OpenCV to detect faces/plates and apply blur to only region which OpenCV specifies