Why are text-to-image AIs in general so bad at recognizing writings?

Forum Thread on r/DALL·E 2

Shedrilledidentitypressed Decre11 created on May 3, 2022

In the subreddit community r/DALL·E 2, a user named sibylazure posted a thought-provoking question: "Why are text-to-image AIs in general so bad at recognizing writings?" The post has garnered 37 votes and 28 comments, sparking a discussion about the limitations of text-to-image AI.

Text-to-Image AIs' Weakness in Recognizing Writings

The userreports their findings that various text-to-image AI models are not good at producing readable or well-written text when shown images. This conversation relates to other old-fashioned text-to-image AI models, such as VQGAN+CLIP and Bigsleep, which are less capable of generating coherent content. However, the user cites DALL·E 2 as an exception, suggesting that it is surprisingly good at producing well-written content, especially with respect to texting.

The user examinesthe writing systems that DALL·E 2 struggles to recognize. They observe that DALL·E 2 has a harder time recognizing writing systems like cursive or traditional Chinese characters. The user speculates that the complexity of these writing systems may be more challenging for the AI.

Discussion Points

1. Text-to-image AIs' generative model limitations: The AI may have trouble generating text that accurately represents the written input due to its generative model's limitations.
2. Writing system complexity: The varying levels of complexity in writing systems, such as strokes, letterforms, and language-specific aspects, can affect the accuracy of text recognition.
3. Data training and volume: The quantity and diversity of data used to train these AIs might influence their ability to recognize specific writing systems.
4. AI model Inconsistencies: Various text-to-image AIs have their own unique strengths and weaknesses, which can be attributed to factors such as their training data, architecture, and model choices.

The discussion reflects the growing awareness and understanding of the capabilities and limitations of AI systems, highlighting the need for improvement and further research in natural language and image generation models.