Abstract
With the advancement of mobile applications, now it is possible to perform instant text translation using a smartphone’s camera. Because text translation within images is still a relatively new field of research, it is not surprising that the translation quality of these mobile applications is under-researched. This study aims to determine the image-to-text translation quality in the English to Lithuanian language direction using popular machine translation apps. To classify errors and evaluate the quality of translation, the present study adopts and customizes the Multidimensional Quality Metrics (MQM) framework (Lommel 2014). The obtained results indicate that image-to-text machine translation apps produce exceptionally low-quality translations for the English-Lithuanian language pair. Therefore, the quality of machine translation for low-resource languages such as Lithuanian remains an issue.
Reference25 articles.
1. Babych, Bogdan. 2014. Automated MT Evaluation Metrics and their Limitations. Tradumàtica 12. 464-470. https://doi.org/10.5565/rev/tradumatica.70
2. Banerjee, Satanjeev, and Lavie, Alon. 2005, June. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments. In: Proceedings of the Acl Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. 65-72. Accessed March 15, 2021. https://aclanthology.org/W05-0909.pdf
3. Canedo-Rodriguez, Adrian, Soohyung Kim, Jung H. Kim, and Yolanda Blanco-Fernandez. 2009, March. English to Spanish Translation of Signboard Images from Mobile Phone Camera. In: IEEE Southeastcon 2009. IEEE, 356-361. https://doi.org/10.1109/secon.2009.5174105
4. Daudaravičius, Vidas. 2006. Pradžia į begalybę. Mašininis vertimas ir lietuvių kalba. Darbai ir dienos, 45. 7-18. Accessed March 21, 2021. https://www.ceeol.com/search/article-detail?id=209872
5. Epshtein, Boris, Eyal Ofek, and Yonatan Wexler. 2010. Detecting Text in Natural Scenes with Stroke Width Transform. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2963-2970. https://doi.org/10.1109/cvpr.2010.5540041