1. Mandaviya, K., Chaudhuri, A., Badelia, P.: Optical Character Recognition Systems for Different Languages with Soft Computing. Springer, Cham (2019)

   Google Scholar 

2. Atienza, R.: Vision transformer for fast and efficient scene text recognition. In: International Conference on Document Analysis and Recognition, pp. 319–334. Springer (2021)

   Google Scholar 

3. Baek, J., et al.: What is wrong with scene text recognition model comparisons? Dataset and model analysis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4715–4723 (2019)

   Google Scholar 

4. Bautista, D., Atienza, R.: Scene text recognition with permuted autoregressive sequence models. In: European Conference on Computer Vision, pp. 178–196. Springer (2022)

   Google Scholar 

5. Borisyuk, F., Gordo, A., Sivakumar, V.: Rosetta: large scale system for text detection and recognition in images. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 71–79 (2018)

   Google Scholar 

6. Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale (2020).arXiv:2010.11929

7. Du, Y., et al.: Svtr: scene text recognition with a single visual model (2022).arXiv:2205.00159

8. Fang, S., Xie, H., Wang, Y., Mao, Z., Zhang, Y.: Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7098–7107 (2021)

   Google Scholar 

9. Fujitake, M.: Dtrocr: Decoder-only transformer for optical character recognition. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 8025–8035 (2024)

   Google Scholar 

10. Hessel, J., Holtzman, A., Forbes, M., Bras, R.L., Choi, Y.: Clipscore: a reference-free evaluation metric for image captioning (2021).arXiv:2104.08718

11. Kim, G., Kwon, T., Ye, J.C.: Diffusionclip: Text-guided diffusion models for robust image manipulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2426–2435 (2022)

    Google Scholar 

12. Li, M., et al.: Trocr: transformer-based optical character recognition with pre-trained models. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 13094–13102 (2023)

    Google Scholar 

13. Luo, H., Ji, L., Zhong, M., Chen, Y., Lei, W., Duan, N., Li, T.: Clip4clip: an empirical study of clip for end to end video clip retrieval and captioning. Neurocomputing508, 293–304 (2022)

    Article  Google Scholar 

14. Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., Lischinski, D.: Styleclip: text-driven manipulation of stylegan imagery. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2085–2094 (2021)

    Google Scholar 

15. Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)

    Google Scholar 

16. Sabu, A.M., Das, A.S.: A survey on various optical character recognition techniques. In: 2018 Conference on Emerging Devices and Smart Systems (ICEDSS), pp. 152–155. IEEE (2018)

    Google Scholar 

17. Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell.39(11), 2298–2304 (2016)

    Article  Google Scholar 

18. Song, H., Dong, L., Zhang, W.N., Liu, T., Wei, F.: Clip models are few-shot learners: empirical studies on vqa and visual entailment (2022).arXiv:2203.07190

19. Subramanian, S., Merrill, W., Darrell, T., Gardner, M., Singh, S., Rohrbach, A.: Reclip: a strong zero-shot baseline for referring expression comprehension (2022).arXiv:2204.05991

20. Wang, P., Da, C., Yao, C.: Multi-granularity prediction for scene text recognition. In: European Conference on Computer Vision, pp. 339–355. Springer (2022)

    Google Scholar 

21. Wang, Z., Xie, H., Wang, Y., Xu, J., Zhang, B., Zhang, Y.: Symmetrical linguistic feature distillation with clip for scene text recognition. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 509–518 (2023)

    Google Scholar 

22. Xu, M., Zhang, J., Xu, L., Silamu, W., Li, Y.: Collaborative encoding method for scene text recognition in low linguistic resources: the Uyghur language case study. Appl. Sci.14(5), 1707 (2024)

    Article  Google Scholar 

23. Zhao, S., Wang, X., Zhu, L., Yang, Y.: Clip4str: A simple baseline for scene text recognition with pre-trained vision-language model (2023).arXiv:2305.14014

24. Zheng, T., Chen, Z., Fang, S., Xie, H., Jiang, Y.G.: Cdistnet: perceiving multi-domain character distance for robust text recognition. Int. J. Comput. Vis. 1–19 (2023)

    Google Scholar 

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