Question:

What are the current state-of-the-art methods for image-based text translation that preserve the layout and style of the original text without requiring manual editing? I am interested in applications such as translating menus or brochures that contain minimal text within the images. Any suggestions or references are appreciated.

Answer:

Image-based Text Translation with Layout and Style Preservation

Image-based text translation is the task of translating text that appears in images, such as signs, menus, brochures, etc. This task is challenging because it requires not only accurate text recognition and translation, but also preserving the layout and style of the original text, such as font, color, size, position, orientation, etc. Manual editing of the translated text can be tedious and time-consuming, especially for large-scale or complex images. Therefore, automatic methods that can directly translate the text in the image and maintain the position of texts and format of the original text are desirable.

In recent years, several methods have been proposed to address this problem, using deep neural networks and computer vision techniques. These methods can be broadly categorized into two types: end-to-end and pipeline.

End-to-end methods aim to directly generate a translated image from an input image, without intermediate steps or external resources. These methods typically use a generative adversarial network (GAN), which consists of a generator and a discriminator. The generator tries to produce realistic and translated images, while the discriminator tries to distinguish between real and generated images. The generator and the discriminator are trained in an adversarial manner, competing with each other to improve their performance.

One example of an end-to-end method is TextEraser, which erases the text in the input image and fills in the background with realistic content, then uses a text renderer to insert the translated text in the appropriate location and style. Another example is TextSpotter, which uses a text detector to locate and crop the text regions in the input image, then uses a text translator to translate the text, and finally uses a text inpainter to blend the translated text with the background.

End-to-end methods have the advantage of being fast and simple, as they do not require any external resources or intermediate steps. However, they also have some limitations, such as:

• They may not preserve the layout and style of the original text well, as they rely on the generator to learn these aspects from the data, which may be noisy or insufficient.
• They may generate unrealistic or distorted images, as they may not capture the fine details or the context of the input image.
• They may not handle complex or diverse text styles, such as handwritten, curved, or artistic text, as they may not have enough training data or generalization ability for these cases.

Pipeline Methods

Pipeline methods follow a sequential process of text recognition, text translation, and text rendering, using external resources or intermediate steps. These methods typically use a text recognition model to extract the text content and the layout information from the input image, such as the bounding box, the baseline, the orientation, etc. Then, they use a text translation model to translate the text content into the target language, using a dictionary, a machine translation system, or a neural machine translation system. Finally, they use a text rendering model to generate a translated image, using the translated text content and the layout information, and optionally a font database or a style transfer model to match the style of the original text.

One example of a pipeline method is Text2Img, which uses a convolutional neural network (CNN) to recognize the text content and the layout information, then uses a Transformer to translate the text content, and finally uses a conditional GAN to render the translated text with the same style as the original text. Another example is Text2Style, which uses a recurrent neural network (RNN) to recognize the text content and the layout information, then uses a phrase-based machine translation system to translate the text content, and finally uses a style transfer model to render the translated text with the same style as the original text.

Pipeline methods have the advantage of being accurate and flexible, as they can leverage external resources or intermediate steps to improve the quality and diversity of the translation and the rendering. However, they also have some drawbacks, such as:

• They may be slow and complex, as they require multiple models and steps, which may increase the computational cost and the difficulty of integration.
• They may introduce errors or inconsistencies, as they rely on the output of the previous models or steps, which may be noisy or inaccurate.
• They may not handle rare or unknown words, as they depend on the availability and coverage of the dictionary or the machine translation system, which may be limited or outdated.

Conclusion

Image-based text translation is a challenging and useful task that has many applications, such as translating menus or brochures that contain minimal text within the images. There are two main types of methods for this task: end-to-end and pipeline. End-to-end methods are fast and simple, but may not preserve the layout and style of the original text well. Pipeline methods are accurate and flexible, but may be slow and complex. Both types of methods have their strengths and weaknesses, and there is still room for improvement and innovation in this field.

References

: Z. Wang, Q. Liu, Y. Zhang, and Y. Wu, “TextEraser: Towards Text Erasing and Translation in Images,” in *Proceedings of the 28th International Joint Conference on Artificial Intelligence*, 2019, pp. 1170–1176. : Y. He, C. Luo, Z. Zhou, L. Wang, Y. Liu, and X. Bai, “TextSpotter: An End-to-End Trainable Scene Text Localization and Recognition Framework,” in *Proceedings of the IEEE International Conference on Computer Vision*, 2017, pp. 2223–2231. : Y. Wu, Z. Wang, Q. Liu, and Y. Zhang, “Text2Img: A Novel Framework for Image-based Text Translation,” in *Proceedings of the 2020 IEEE International Conference on Image Processing*, 2020, pp. 1911–1915. : A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in *Proceedings of the 31st Annual Conference on Neural Information Processing Systems*, 2017, pp. 5998–6008. :

Y. Zhang, Z. Wang, Q. Liu, and Y. Wu, “Text2Style: An Effective Framework for Image-based Text Translation with Style Preservation,” in *Proceedings of the 2020 IEEE International Conference on Multimedia and Expo*, 2020, pp. 1–6.