# Query by String word spotting based on character bi-gram indexing

arXiv: https://arxiv.org/abs/1505.07778 · HTML: https://mlsanity.com/paper/1505.07778/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2015.

## Built on by (most cited first, 4 of 4)

- [Scene Text Retrieval via Joint Text Detection and Similarity Learning](https://mlsanity.com/paper/2104.01552/) (2021)
- [Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text Retrieval](https://mlsanity.com/paper/2408.00441/) (2024)
- [PILOT: A Promptable Interleaved Layout-aware OCR Transformer](https://mlsanity.com/paper/2504.03621/) (2025)
- [Keyword Spotting Simplified: A Segmentation-Free Approach using Character Counting and CTC re-scoring](https://mlsanity.com/paper/2308.03515/) (2023)

## Abstract (arXiv)

In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC). These attribute models are learned using linear SVMs over the Fisher Vector representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi- gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasets