# Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network

arXiv: https://arxiv.org/abs/1801.07211 · HTML: https://mlsanity.com/paper/1801.07211/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2018.

## Built on by (most cited first, 7 of 7)

- [Complex Handwriting Trajectory Recovery: Evaluation Metrics and Algorithm](https://mlsanity.com/paper/2210.15879/) (2022)
- [TRACE: A Differentiable Approach to Line-level Stroke Recovery for Offline Handwritten Text](https://mlsanity.com/paper/2105.11559/) (2021)
- [Writing Order Recovery in Complex and Long Static Handwriting](https://mlsanity.com/paper/2406.03194/) (2024)
- [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)
- [Handwriting Trajectory Recovery with Diffusion Models](https://mlsanity.com/paper/2607.03422/) (2026)
- [Handwriting Trajectory Recovery via Autoregressive Ordered Stroke Instance Prediction](https://mlsanity.com/paper/2609.02251/) (2026)

## Abstract (arXiv)

In this paper, we introduce a novel technique to recover the pen trajectory of offline characters which is a crucial step for handwritten character recognition. Generally, online acquisition approach has more advantage than its offline counterpart as the online technique keeps track of the pen movement. Hence, pen tip trajectory retrieval from offline text can bridge the gap between online and offline methods. Our proposed framework employs sequence to sequence model which consists of an encoder-decoder LSTM module. Our encoder module consists of Convolutional LSTM network, which takes an offline character image as the input and encodes the feature sequence to a hidden representation. The output of the encoder is fed to a decoder LSTM and we get the successive coordinate points from every time step of the decoder LSTM. Although the sequence to sequence model is a popular paradigm in various computer vision and language translation tasks, the main contribution of our work lies in designing an end-to-end network for a decade old popular problem in Document Image Analysis community. Tamil, Telugu and Devanagari characters of LIPI Toolkit dataset are used for our experiments. Our proposed method has achieved superior performance compared to the other conventional approaches.