Learning Moderately Input-Sensitive Functions: A Case Study in QR Code Decoding

• URL: http://arxiv.org/abs/2506.20305v1
• Date: Wed, 25 Jun 2025 10:37:39 GMT
• Title: Learning Moderately Input-Sensitive Functions: A Case Study in QR Code Decoding
• Authors: Kazuki Yoda, Kazuhiko Kawamoto, Hiroshi Kera,
• Abstract summary: This study presents the first learning-based Quick Response (QR) code decoding.<n>Experiments reveal that Transformers can successfully decode QR codes, even beyond the theoretical error-correction limit.
• Score: 5.2980803808373516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The hardness of learning a function that attains a target task relates to its input-sensitivity. For example, image classification tasks are input-insensitive as minor corruptions should not affect the classification results, whereas arithmetic and symbolic computation, which have been recently attracting interest, are highly input-sensitive as each input variable connects to the computation results. This study presents the first learning-based Quick Response (QR) code decoding and investigates learning functions of medium sensitivity. Our experiments reveal that Transformers can successfully decode QR codes, even beyond the theoretical error-correction limit, by learning the structure of embedded texts. They generalize from English-rich training data to other languages and even random strings. Moreover, we observe that the Transformer-based QR decoder focuses on data bits while ignoring error-correction bits, suggesting a decoding mechanism distinct from standard QR code readers.

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