BPU-Net: a precise grain image segmentation method based on bidirectional skip connections and continuous VI loss

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confusable-vision takes the 1,418 TR39 confusable pairs that map a non-Latin character to a Latin target (a-z, 0-9), renders both characters across every available system font, and computes SSIM for each pairing. The output is a scored JSON artifact: one continuous similarity score per pair, per font.