  Video captioning in Nepali, a language written in the Devanagari script,
presents a unique challenge due to the lack of existing academic work in this
domain. This work develops a novel encoder-decoder paradigm for Nepali video
captioning to tackle this difficulty. LSTM and GRU sequence-to-sequence models
are used in the model to produce related textual descriptions based on features
retrieved from video frames using CNNs. Using Google Translate and manual
post-editing, a Nepali video captioning dataset is generated from the Microsoft
Research Video Description Corpus (MSVD) dataset created using Google
Translate, and manual post-editing work. The efficiency of the model for
Devanagari-scripted video captioning is demonstrated by BLEU, METOR, and ROUGE
measures, which are used to assess its performance.
