Field Typing for Improved Recognition on Heterogeneous Handwritten Forms Offline handwriting recognition has undergone continuous progress over the past decades. However, existing methods are typically benchmarked on freeform text datasets that are biased towards goodquality images and handwriting styles, and homogeneous content. In this paper, we show that stateoftheart algorithms, employing long shortterm memory (LSTM) layers, do not readily generalize to realworld structured documents, such as forms, due to their highly heterogeneous and outofvocabulary content, and to the inherent ambiguities of this content. To address this, we propose to leverage the content type within an LSTMbased architecture. Furthermore, we introduce a procedure to generate synthetic data to train this architecture without requiring expensive manual annotations. We demonstrate the effectiveness of our approach at transcribing text on a challenging, realworld dataset of European Accident Statements.

A Scalable Handwritten Text Recognition System Many studies on (Offline) Handwritten Text Recognition (HTR) systems have focused on building stateoftheart models for line recognition on small corpora. However, adding HTR capability to a large scale multilingual OCR system poses new challenges. This paper addresses three problems in building such systems: data, efficiency, and integration. Firstly, one of the biggest challenges is obtaining sufficient amounts of high quality training data. We address the problem by using online handwriting data collected for a large scale production online handwriting recognition system. We describe our image data generation pipeline and study how online data can be used to build HTR models. We show that the data improve the models significantly under the condition where only a small number of real images is available, which is usually the case for HTR models. It enables us to support a new script at substantially lower cost. Secondly, we propose a line recognition model based on neural networks without recurrent connections. The model achieves a comparable accuracy with LSTMbased models while allowing for better parallelism in training and inference. Finally, we present a simple way to integrate HTR models into an OCR system. These constitute a solution to bring HTR capability into a large scale OCR system.