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Due to privacy issues and the need for validation by interpreters, creating large-scale sign language (SL) datasets can be difficult. The authors address this by presenting a new Spanish isolated sign language recognition dataset, CALSE-1000, consisting of 5000 videos representing 1000 glosses, with various signers and scenarios. The study also proposes using different computer vision techniques, such as face swapping and affine transformations, to augment the SL dataset and improve the accuracy of the model I3D trained using them. The results show that the inclusion of these augmentations during training leads to an improvement in accuracy in top-1 metrics by up to 11.7 points, top-5 by up to 8.8 points and top-10 by up to 9 points. This has great potential to improve the state of the art in other datasets and other models. 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