# A Scalable Handwritten Text Recognition System

arXiv: https://arxiv.org/abs/1904.09150 · HTML: https://mlsanity.com/paper/1904.09150/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 25 of 25)

- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [Rethinking Text Line Recognition Models](https://mlsanity.com/paper/2104.07787/) (2021)
- [Lexically Aware Semi-Supervised Learning for OCR Post-Correction](https://mlsanity.com/paper/2111.02622/) (2021)
- [Handwritten Text Recognition: A Survey](https://mlsanity.com/paper/2502.08417/) (2025)
- [KOHTD: Kazakh Offline Handwritten Text Dataset](https://mlsanity.com/paper/2110.04075/) (2021)
- [On the Generalization of Handwritten Text Recognition Models](https://mlsanity.com/paper/2411.17332/) (2024)
- [Unified Line and Paragraph Detection by Graph Convolutional Networks](https://mlsanity.com/paper/2203.09638/) (2022)
- [Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition](https://mlsanity.com/paper/2202.07901/) (2022)
- [Easter2.0: Improving convolutional models for handwritten text recognition](https://mlsanity.com/paper/2205.14879/) (2022)
- [OCR Improves Machine Translation for Low-Resource Languages](https://mlsanity.com/paper/2202.13274/) (2022)
- [Weakly supervised information extraction from inscrutable handwritten document images](https://mlsanity.com/paper/2306.06823/) (2023)
- [Lacuna Reconstruction: Self-supervised Pre-training for Low-Resource Historical Document Transcription](https://mlsanity.com/paper/2112.08692/) (2021)
- [Is it an i or an l: Test-time Adaptation of Text Line Recognition Models](https://mlsanity.com/paper/2308.15037/) (2023)
- [Recurrent neural network transducer for Japanese and Chinese offline handwritten text recognition](https://mlsanity.com/paper/2106.14459/) (2021)
- [StackMix and Blot Augmentations for Handwritten Text Recognition](https://mlsanity.com/paper/2108.11667/) (2021)
- [Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine Learning in Epigraphy](https://mlsanity.com/paper/2310.07310/) (2023)
- [A Transformer-based Math Language Model for Handwritten Math Expression Recognition](https://mlsanity.com/paper/2108.05002/) (2021)
- [Handwritten text generation and strikethrough characters augmentation](https://mlsanity.com/paper/2112.07395/) (2021)
- [HWRCNet: Handwritten Word Recognition in JPEG Compressed Domain using CNN-BiLSTM Network](https://mlsanity.com/paper/2201.00947/) (2022)
- [CONSTANT: Towards High-Quality One-Shot Handwriting Generation with Patch Contrastive Enhancement and Style-Aware Quantization](https://mlsanity.com/paper/2603.07543/) (2026)
- [Motion-Based Handwriting Recognition and Word Reconstruction](https://mlsanity.com/paper/2101.06025/) (2021)
- [Adaptive Shrink-Mask for Text Detection](https://mlsanity.com/paper/2111.09560/) (2021)
- [Towards Robust Handwritten Text Recognition with On-the-fly User Participation](https://mlsanity.com/paper/2212.08834/) (2022)
- [An end-to-end, interactive Deep Learning based Annotation system for cursive and print English handwritten text](https://mlsanity.com/paper/2304.08670/) (2023)
- [Align, Minimize and Diversify: A Source-Free Unsupervised Domain Adaptation Method for Handwritten Text Recognition](https://mlsanity.com/paper/2404.18260/) (2024)

## Abstract (arXiv)

Many studies on (Offline) Handwritten Text Recognition (HTR) systems have focused on building state-of-the-art 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 LSTM-based 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.