# VML-MOC: Segmenting a multiply oriented and curved handwritten text lines dataset

arXiv: https://arxiv.org/abs/2101.07542 · HTML: https://mlsanity.com/paper/2101.07542/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 19 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Multi-Oriented Text Detection with Fully Convolutional Networks](https://mlsanity.com/paper/1604.04018/) (2016)
- [Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks](https://mlsanity.com/paper/1804.00236/) (2018)

## Built on by (most cited first, 1 of 1)

- [Unsupervised learning of text line segmentation by differentiating coarse patterns](https://mlsanity.com/paper/2105.09405/) (2021)

## Abstract (arXiv)

This paper publishes a natural and very complicated dataset of handwritten documents with multiply oriented and curved text lines, namely VML-MOC dataset. These text lines were written as remarks on the page margins by different writers over the years. They appear at different locations within the orientations that range between 0 and 180 or as curvilinear forms. We evaluate a multi-oriented Gaussian based method to segment these handwritten text lines that are skewed or curved in any orientation. It achieves a mean pixel Intersection over Union score of 80.96% on the test documents. The results are compared with the results of a single-oriented Gaussian based text line segmentation method.