# Fast Multi-language LSTM-based Online Handwriting Recognition

arXiv: https://arxiv.org/abs/1902.10525 · HTML: https://mlsanity.com/paper/1902.10525/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2019.

## Built on by (most cited first, 22 of 22)

- [Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting](https://mlsanity.com/paper/2103.13716/) (2021)
- [MathWriting: A Dataset For Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2404.10690/) (2024)
- [Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens](https://mlsanity.com/paper/2202.07036/) (2022)
- [Recognizing Vector Graphics without Rasterization](https://mlsanity.com/paper/2111.03281/) (2021)
- [Representing Online Handwriting for Recognition in Large Vision-Language Models](https://mlsanity.com/paper/2402.15307/) (2024)
- [Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition](https://mlsanity.com/paper/2202.07901/) (2022)
- [An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic](https://mlsanity.com/paper/2101.01373/) (2021)
- [Towards an IMU-based Pen Online Handwriting Recognizer](https://mlsanity.com/paper/2105.12434/) (2021)
- [Transformers versus LSTMs for electronic trading](https://mlsanity.com/paper/2309.11400/) (2023)
- [TRACE: A Differentiable Approach to Line-level Stroke Recovery for Offline Handwritten Text](https://mlsanity.com/paper/2105.11559/) (2021)
- [Character Queries: A Transformer-based Approach to On-Line Handwritten Character Segmentation](https://mlsanity.com/paper/2309.03072/) (2023)
- [Self-Supervised Representation Learning for Online Handwriting Text Classification](https://mlsanity.com/paper/2310.06645/) (2023)
- [DSS: Synthesizing long Digital Ink using Data augmentation, Style encoding and Split generation](https://mlsanity.com/paper/2311.17786/) (2023)
- [InkFM: A Foundational Model for Full-Page Online Handwritten Note Understanding](https://mlsanity.com/paper/2503.23081/) (2025)
- [Writing in The Air: Unconstrained Text Recognition from Finger Movement Using Spatio-Temporal Convolution](https://mlsanity.com/paper/2104.09021/) (2021)
- [Data Incubation -- Synthesizing Missing Data for Handwriting Recognition](https://mlsanity.com/paper/2110.07040/) (2021)
- [LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks](https://mlsanity.com/paper/2301.04794/) (2023)
- [Sampling and Ranking for Digital Ink Generation on a tight computational budget](https://mlsanity.com/paper/2306.03103/) (2023)
- [Robust and Efficient Writer-Independent IMU-Based Handwriting Recognition](https://mlsanity.com/paper/2502.20954/) (2025)
- [Preserving Privacy Without Compromising Accuracy: Machine Unlearning for Handwritten Text Recognition](https://mlsanity.com/paper/2504.08616/) (2025)
- [ScribeTokens: Fixed-Vocabulary Tokenization of Digital Ink](https://mlsanity.com/paper/2603.02805/) (2026)
- [Tokenization vs. Augmentation: A Systematic Study of Writer Variance in IMU-Based Online Handwriting Recognition](https://mlsanity.com/paper/2603.16883/) (2026)

## Abstract (arXiv)

We describe an online handwriting system that is able to support 102 languages using a deep neural network architecture. This new system has completely replaced our previous Segment-and-Decode-based system and reduced the error rate by 20%-40% relative for most languages. Further, we report new state-of-the-art results on IAM-OnDB for both the open and closed dataset setting. The system combines methods from sequence recognition with a new input encoding using B'ezier curves. This leads to up to 10x faster recognition times compared to our previous system. Through a series of experiments we determine the optimal configuration of our models and report the results of our setup on a number of additional public datasets.