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Leveraging the characteristics of convolutional layers, neural networks areextremely effective for pattern recognition tasks. However in some cases, theirdecisions are based on unintended information leading to high performance onstandard benchmarks but also to a lack of generalization to challenging testingconditions and unintuitive failures. Recent work has termed this "shortcutlearning" and addressed its presence in multiple domains. In text recognition,we reveal another such shortcut, whereby recognizers overly depend on localimage statistics. Motivated by this, we suggest an approach to regulate thereliance on local statistics that improves text recognition performance.  Our method, termed TextAdaIN, creates local distortions in the feature mapwhich prevent the network from overfitting to local statistics. It does so byviewing each feature map as a sequence of elements and deliberately mismatchingfine-grained feature statistics between elements in a mini-batch. DespiteTextAdaIN's simplicity, extensive experiments show its effectiveness comparedto other, more complicated methods. TextAdaIN achieves state-of-the-art resultson standard handwritten text recognition benchmarks. It generalizes to multiplearchitectures and to the domain of scene text recognition. Furthermore, wedemonstrate that integrating TextAdaIN improves robustness towards morechallenging testing conditions. The official Pytorch implementation can befound at https://github.com/amazon-research/textadain-robust-recognition.

TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers