# TRACE: A Differentiable Approach to Line-level Stroke Recovery for Offline Handwritten Text

arXiv: https://arxiv.org/abs/2105.11559 · HTML: https://mlsanity.com/paper/2105.11559/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Analyzing Inverse Problems with Invertible Neural Networks](https://mlsanity.com/paper/1808.04730/) (2018)
- [ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation](https://mlsanity.com/paper/2003.10557/) (2020)
- [Text and Style Conditioned GAN for Generation of Offline Handwriting Lines](https://mlsanity.com/paper/2009.00678/) (2020)
- [Fast Multi-language LSTM-based Online Handwriting Recognition](https://mlsanity.com/paper/1902.10525/) (2019)
- [Stochastic Backpropagation through Mixture Density Distributions](https://mlsanity.com/paper/1607.05690/) (2016)
- [Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders](https://mlsanity.com/paper/1906.06142/) (2019)
- [Online trajectory recovery from offline handwritten Japanese kanji characters](https://mlsanity.com/paper/2009.04284/) (2020)
- [Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network](https://mlsanity.com/paper/1801.07211/) (2018)
- [Indic Handwritten Script Identification using Offline-Online Multimodal Deep Network](https://mlsanity.com/paper/1802.08568/) (2018)

## Built on by (most cited first, 3 of 3)

- [Complex Handwriting Trajectory Recovery: Evaluation Metrics and Algorithm](https://mlsanity.com/paper/2210.15879/) (2022)
- [InkSight: Offline-to-Online Handwriting Conversion by Teaching Vision-Language Models to Read and Write](https://mlsanity.com/paper/2402.05804/) (2024)
- [Layout Stroke Imitation: A Layout Guided Handwriting Stroke Generation for Style Imitation with Diffusion Model](https://mlsanity.com/paper/2509.15678/) (2025)

## Abstract (arXiv)

Stroke order and velocity are helpful features in the fields of signature verification, handwriting recognition, and handwriting synthesis. Recovering these features from offline handwritten text is a challenging and well-studied problem. We propose a new model called TRACE (Trajectory Recovery by an Adaptively-trained Convolutional Encoder). TRACE is a differentiable approach that uses a convolutional recurrent neural network (CRNN) to infer temporal stroke information from long lines of offline handwritten text with many characters and dynamic time warping (DTW) to align predictions and ground truth points. TRACE is perhaps the first system to be trained end-to-end on entire lines of text of arbitrary width and does not require the use of dynamic exemplars. Moreover, the system does not require images to undergo any pre-processing, nor do the predictions require any post-processing. Consequently, the recovered trajectory is differentiable and can be used as a loss function for other tasks, including synthesizing offline handwritten text. We demonstrate that temporal stroke information recovered by TRACE from offline data can be used for handwriting synthesis and establish the first benchmarks for a stroke trajectory recovery system trained on the IAM online handwriting dataset.