# Field typing for improved recognition on heterogeneous handwritten forms

arXiv: https://arxiv.org/abs/1909.10120 · HTML: https://mlsanity.com/paper/1909.10120/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [Writing Order Recovery in Complex and Long Static Handwriting](https://mlsanity.com/paper/2406.03194/) (2024)
- [VOLTAGE: A Versatile Contrastive Learning based OCR Methodology for ultra low-resource scripts through Auto Glyph Feature Extraction](https://mlsanity.com/paper/2510.10490/) (2025)

## Abstract (arXiv)

Offline handwriting recognition has undergone continuous progress over the past decades. However, existing methods are typically benchmarked on free-form text datasets that are biased towards good-quality images and handwriting styles, and homogeneous content. In this paper, we show that state-of-the-art algorithms, employing long short-term memory (LSTM) layers, do not readily generalize to real-world structured documents, such as forms, due to their highly heterogeneous and out-of-vocabulary content, and to the inherent ambiguities of this content. To address this, we propose to leverage the content type within an LSTM-based architecture. Furthermore, we introduce a procedure to generate synthetic data to train this architecture without requiring expensive manual annotations. We demonstrate the effectiveness of our approach at transcribing text on a challenging, real-world dataset of European Accident Statements.