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.

Training FullPage Handwritten Text Recognition Models without Annotated Line Breaks Training Handwritten Text Recognition (HTR) models typically requires large amounts of labeled data which often are line or page images with corresponding linelevel ground truth (GT) transcriptions. Many digital collections have pagelevel transcriptions for each image, but the transcription is unformatted, i.e., line breaks are not annotated. Can we train linedbased HTR models using such data? In this work, we present a novel alignment technique for segmenting pagelevel GT text into text lines during HTR model training. This text segmentation problem is formulated as an optimization problem to minimize the cost of aligning predicted lines with the GT text. Using both simulated and HTR model predictions, we show that the alignment method identifies line breaks accurately, even when the predicted lines have high character error rates (CER). We removed the GT line breaks from the ICDAR2017 READ dataset and trained a HTR model using the proposed alignment method to predict line breaks onthefly. This model achieves comparable CER w.r.t. to the same model trained with the GT line breaks. Additionally, we downloaded an online digital collection of 50K English journal pages (not curated for HTR research) whose transcriptions do not contain line breaks, and achieve 11% CER.