# PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents

arXiv: https://arxiv.org/abs/1604.00187 · HTML: https://mlsanity.com/paper/1604.00187/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2016.

## Built on by (most cited first, 18 of 18)

- [A Survey of Historical Document Image Datasets](https://mlsanity.com/paper/2203.08504/) (2022)
- [Best Practices for a Handwritten Text Recognition System](https://mlsanity.com/paper/2404.11339/) (2024)
- [Scene Text Retrieval via Joint Text Detection and Similarity Learning](https://mlsanity.com/paper/2104.01552/) (2021)
- [Asking questions on handwritten document collections](https://mlsanity.com/paper/2110.00711/) (2021)
- [Towards End-to-End Explainable Facial Action Unit Recognition via Vision-Language Joint Learning](https://mlsanity.com/paper/2408.00644/) (2024)
- [Multi-Granularity Prediction with Learnable Fusion for Scene Text Recognition](https://mlsanity.com/paper/2307.13244/) (2023)
- [Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text Retrieval](https://mlsanity.com/paper/2408.00441/) (2024)
- [Recognition-free Question Answering on Handwritten Document Collections](https://mlsanity.com/paper/2202.06080/) (2022)
- [ST-KeyS: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents](https://mlsanity.com/paper/2303.03127/) (2023)
- [Partial Scene Text Retrieval](https://mlsanity.com/paper/2411.10261/) (2024)
- [Pho(SC)-CTC -- A Hybrid Approach Towards Zero-shot Word Image Recognition](https://mlsanity.com/paper/2105.15093/) (2021)
- [A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts](https://mlsanity.com/paper/2209.10441/) (2022)
- [PILOT: A Promptable Interleaved Layout-aware OCR Transformer](https://mlsanity.com/paper/2504.03621/) (2025)
- [Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting](https://mlsanity.com/paper/2106.05144/) (2021)
- [Keyword Spotting Simplified: A Segmentation-Free Approach using Character Counting and CTC re-scoring](https://mlsanity.com/paper/2308.03515/) (2023)
- [TextMatcher: Cross-Attentional Neural Network to Compare Image and Text](https://mlsanity.com/paper/2205.05507/) (2022)
- [Unsupervised Domain Adaptation for Symbol Spotting in Historical Encrypted Manuscripts](https://mlsanity.com/paper/2609.07159/) (2026)
- [HPMD: A Historical Persian Manuscript Dataset for Word Spotting with Line-Level Annotation](https://mlsanity.com/paper/2609.34490/) (2026)

## Abstract (arXiv)

In recent years, deep convolutional neural networks have achieved state of the art performance in various computer vision task such as classification, detection or segmentation. Due to their outstanding performance, CNNs are more and more used in the field of document image analysis as well. In this work, we present a CNN architecture that is trained with the recently proposed PHOC representation. We show empirically that our CNN architecture is able to outperform state of the art results for various word spotting benchmarks while exhibiting short training and test times.