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Advancements in computer vision research have put transformer architecture as the state-of-the-art in computer vision tasks. One of the known drawbacks of the transformer architecture is the high number of parameters, this can lead to a more complex and inefficient algorithm. This paper aims to reduce the number of parameters and in turn, made the transformer more efficient. We present Sparse Transformer (SparTa) Block, a modified transformer block with an addition of a sparse token converter that reduces the dimension of high-level features to the number of latent tokens. We implemented the SparTa Block within the Swin-T architecture (SparseSwin) to leverage Swin's proficiency in extracting low-level features and enhance its capability to extract information from high-level features while reducing the number of parameters. The proposed SparseSwin model outperforms other state-of-the-art models in image classification with an accuracy of 87.26%, 97.43%, and 85.35% on the ImageNet100, CIFAR10, and CIFAR100 datasets respectively. Despite its fewer parameters, the result highlights the potential of a transformer architecture using a sparse token converter with a limited number of tokens to optimize the use of the transformer and improve its performance. The code is available at https://github.com/KrisnaPinasthika/SparseSwin. • Proposing Sparse Transformer Block with limited latent tokens to enhance Swin Transformer efficiency and performance. • We examined the effect of regularization on the attention weight obtained in SparTa Block. • We obtained accuracy improvement on ImageNet100, CIFAR10, and CIFAR100 benchmark datasets. • We obtained a smaller parameter size compared to the state-of-the-art Transformer model. [ABSTRACT FROM AUTHOR]