Sign language translation (SLT) has witnessed rapid progress in the deep learning community across several sign languages, including German, American, British, and Italian. However, Indian Sign Language (ISL) remains relatively underexplored. Motivated by recent efforts to develop large-scale ISL resources, we investigate how existing SLT models perform on ISL data. Specifically, we evaluate three approaches: (i) training a transformer-based model, (ii) leveraging visual-language pretraining, and (iii) tuning a language model with pre-trained visual and motion representations. Unlike existing methods that primarily use raw video frames, we augment the model with optical flow maps to explicitly capture motion primitives, combined with a multi-scale feature extraction method for encoding spatial features (SpaMo-OF). Our approach achieves promising results, obtaining a BLEU-4 score of 8.58 on the iSign test set, establishing a strong baseline for future ISL translation research.