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Offline handwriting recognition (HWR) has improved significantly with theadvent of deep learning architectures in recent years. Nevertheless, it remainsa challenging problem and practical applications often rely on post-processingtechniques for restricting the predicted words via lexicons or language models.Despite their enhanced performance, such systems are less usable in contextswhere out-of-vocabulary words are anticipated, e.g. for detecting misspelledwords in school assessments. To that end, we introduce the task of comparing ahandwriting image to text. To solve the problem, we propose an unrestrictedbinary classifier, consisting of a HWR feature extractor and a multimodalclassification head which convolves the feature extractor output with thevector representation of the input text. Our model's classification head istrained entirely on synthetic data created using a state-of-the-art generativeadversarial network. We demonstrate that, while maintaining high recall, theclassifier can be calibrated to achieve an average precision increase of 19.5%compared to addressing the task by directly using state-of-the-art HWR models.Such massive performance gains can lead to significant productivity increasesin applications utilizing human-in-the-loop automation.

Offline Detection of Misspelled Handwritten Words by Convolving Recognition Model Features with Text Labels