# A Computationally Efficient Pipeline Approach to Full Page Offline Handwritten Text Recognition

arXiv: https://arxiv.org/abs/1910.00663 · HTML: https://mlsanity.com/paper/1910.00663/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 9 of 9)

- [End-to-end Document Recognition and Understanding with Dessurt](https://mlsanity.com/paper/2203.16618/) (2022)
- [DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition](https://mlsanity.com/paper/2203.12273/) (2022)
- [SPAN: a Simple Predict & Align Network for Handwritten Paragraph Recognition](https://mlsanity.com/paper/2102.08742/) (2021)
- [Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-end Handwritten Document Recognition](https://mlsanity.com/paper/2301.10593/) (2023)
- [PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition](https://mlsanity.com/paper/2207.14807/) (2022)
- [Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition](https://mlsanity.com/paper/2202.07901/) (2022)
- [DeepScribe: Localization and Classification of Elamite Cuneiform Signs Via Deep Learning](https://mlsanity.com/paper/2306.01268/) (2023)
- [RVAFM: Re-parameterizing Vertical Attention Fusion Module for Handwritten Paragraph Text Recognition](https://mlsanity.com/paper/2503.03104/) (2025)
- [Block-level Text Spotting with LLMs](https://mlsanity.com/paper/2406.13208/) (2024)

## Abstract (arXiv)

Offline handwriting recognition with deep neural networks is usually limited to words or lines due to large computational costs. In this paper, a less computationally expensive full page offline handwritten text recognition framework is introduced. This framework includes a pipeline that locates handwritten text with an object detection neural network and recognises the text within the detected regions using features extracted with a multi-scale convolutional neural network (CNN) fed into a bidirectional long short term memory (LSTM) network. This framework achieves comparable error rates to state of the art frameworks while using less memory and time. The results in this paper demonstrate the potential of this framework and future work can investigate production ready and deployable handwritten text recognisers.