•1 min read•from Machine Learning
Detecting mirrored selfie images: OCR the best way? [D]
Our take
Detecting mirrored selfie images presents a unique challenge, particularly when preparing them for a visual language model (VLM) focused on text reading and face embedding extraction. Given that models like Qwen and Florence are trained on augmented flipped data, they struggle with backwards text. A promising strategy involves utilizing EasyOCR to assess text crops, comparing the read scores of normal versus flipped versions.
I'm trying to catch backwards "selfie" images before passing them to our VLM text reader and/or face embedding extraction. Since models like Qwen and Florence are trained on flipped data, they are mostly blind to backwards text and prompting them just seems to be fighting against their base training (i'm assuming they used lots of augmented flipped training data). My best idea right now is to run EasyOCR on the text crops and see if the normal or flipped version gets a higher read score. Is this OCR score trick really the best way to handle this, or is there a smart, small model approach I'm missing?
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