Learnings from training a font recognition model from scratch

a day ago

The author trained a font recognition model named Lens to identify the closest open-source Google Font from an image without manual input.
Existing font recognition tools often rely on proprietary fonts and require manual letterform selection, which the author aimed to overcome.
The model Lens processes images in 2-3 seconds, handling various font weights, styles, and image qualities.
The author learned that a 'model' encompasses more than a trained file, involving multiple preprocessing and postprocessing steps.
Data collection and cleaning were identified as the most time-consuming parts of the project, emphasizing the importance of high-quality training data.
Optimizing the training process involved separating CPU and GPU tasks to improve efficiency and starting with a small dataset to iterate quickly.
Challenges included uploading large datasets to the cloud and long iteration cycles, prompting the need for faster data processing and training methods.
Despite technical success, distributing the model and gaining attention remained a significant challenge.
Future plans include improving the model, making it more widely available, and exploring other typography and design-related AI models.

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