Tibetan handwritten text recognition holds significant importance in the fields of Tibetan office automation and ancient document preservation. Addressing the diverse characteristics of Tibetan handwritten characters, this paper proposes a Tibetan handwritten recognition method based on Structural re-parameterization Vision Transformer (ViT) and vertical attention mechanism. The contributions of this paper lie in three aspects: firstly, leveraging Structural re-parameterization technique to construct a feature extraction network, globally modeling image features, and employing an improved vertical attention mechanism to unfold text feature lines, trained using the standard Connectionist Temporal Classification (CTC) loss function. Secondly, in terms of data augmentation, an Adaptive Random Masking (AdaRM) method is proposed based on Tibetan characteristics, effectively enhancing model performance and training efficiency. Lastly, in terms of evaluation metrics, a Tibetan Syllable Error Rate (TSER) metric is proposed based on Tibetan characteristics. Experimental results demonstrate that the proposed method achieves a Character Error Rate (CER) of 4.19% and a TSER of 5.92% on the Tibetan_HW text line recognition test set, and a CER of 3.56% and a TSER of 4.86% on the Tibetan_HW paragraph recognition test set. Compared to baseline models, the recognition error rates are significantly reduced.