# R-PHOC: Segmentation-Free Word Spotting using CNN

arXiv: https://arxiv.org/abs/1707.01294 · HTML: https://mlsanity.com/paper/1707.01294/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2017.

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

- [Keyword Spotting Simplified: A Segmentation-Free Approach using Character Counting and CTC re-scoring](https://mlsanity.com/paper/2308.03515/) (2023)

## Abstract (arXiv)

This paper proposes a region based convolutional neural network for segmentation-free word spotting. Our net- work takes as input an image and a set of word candidate bound- ing boxes and embeds all bounding boxes into an embedding space, where word spotting can be casted as a simple nearest neighbour search between the query representation and each of the candidate bounding boxes. We make use of PHOC embedding as it has previously achieved significant success in segmentation- based word spotting. Word candidates are generated using a simple procedure based on grouping connected components using some spatial constraints. Experiments show that R-PHOC which operates on images directly can improve the current state-of- the-art in the standard GW dataset and performs as good as PHOCNET in some cases designed for segmentation based word spotting.