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Offline handwriting recognition has undergone continuous progress over thepast decades. However, existing methods are typically benchmarked on free-formtext datasets that are biased towards good-quality images and handwritingstyles, and homogeneous content. In this paper, we show that state-of-the-artalgorithms, employing long short-term memory (LSTM) layers, do not readilygeneralize to real-world structured documents, such as forms, due to theirhighly heterogeneous and out-of-vocabulary content, and to the inherentambiguities of this content. To address this, we propose to leverage thecontent type within an LSTM-based architecture. Furthermore, we introduce aprocedure to generate synthetic data to train this architecture withoutrequiring expensive manual annotations. We demonstrate the effectiveness of ourapproach at transcribing text on a challenging, real-world dataset of EuropeanAccident Statements.

Field typing for improved recognition on heterogeneous handwritten forms