Scene text recognition has historically concentrated on English, with limited advancements in developing solutions that perform well across multiple languages. Previous efforts in multilingual scene text recognition have predominantly targeted languages with considerable syntactic and semantic differences. However, Indian languages, while diverse, share numerous common features that remain largely underutilized. This competition aims to address the often-overlooked challenge of scene text recognition within the Indian context and to advance robust word image recognition across ten Indian languages. The dataset provided for this competition is one of the most comprehensive multilingual datasets, encompassing 10 languages, each with 17,500 training samples, 2,500 validation samples and 5,000 test word-image samples. The task was to correctly recognize the word-images, for which we received forty-nine registrations and five final submissions from industrial and research communities. The winning team achieved an average Character Recognition Rate (CRR) of 92.85% and a Word Recognition Rate (WRR) of 84.01% across the ten languages. This paper details the proposed dataset and summarizes the submissions for the competition- WIRIndic-2024.