# Keyword Spotting Simplified: A Segmentation-Free Approach using Character Counting and CTC re-scoring

arXiv: https://arxiv.org/abs/2308.03515 · HTML: https://mlsanity.com/paper/2308.03515/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes](https://mlsanity.com/paper/1908.08207/) (2019)
- [Text Spotting Transformers](https://mlsanity.com/paper/2204.01918/) (2022)
- [PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents](https://mlsanity.com/paper/1604.00187/) (2016)
- [HWNet v2: An Efficient Word Image Representation for Handwritten Documents](https://mlsanity.com/paper/1802.06194/) (2018)
- [Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting in Handwritten Manuscript Collections](https://mlsanity.com/paper/1703.07645/) (2017)
- [Query by String word spotting based on character bi-gram indexing](https://mlsanity.com/paper/1505.07778/) (2015)
- [R-PHOC: Segmentation-Free Word Spotting using CNN](https://mlsanity.com/paper/1707.01294/) (2017)

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

- [PILOT: A Promptable Interleaved Layout-aware OCR Transformer](https://mlsanity.com/paper/2504.03621/) (2025)

## Abstract (arXiv)

Recent advances in segmentation-free keyword spotting treat this problem w.r.t. an object detection paradigm and borrow from state-of-the-art detection systems to simultaneously propose a word bounding box proposal mechanism and compute a corresponding representation. Contrary to the norm of such methods that rely on complex and large DNN models, we propose a novel segmentation-free system that efficiently scans a document image to find rectangular areas that include the query information. The underlying model is simple and compact, predicting character occurrences over rectangular areas through an implicitly learned scale map, trained on word-level annotated images. The proposed document scanning is then performed using this character counting in a cost-effective manner via integral images and binary search. Finally, the retrieval similarity by character counting is refined by a pyramidal representation and a CTC-based re-scoring algorithm, fully utilizing the trained CNN model. Experimental validation on two widely-used datasets shows that our method achieves state-of-the-art results outperforming the more complex alternatives, despite the simplicity of the underlying model.