# Pho(SC)-CTC -- A Hybrid Approach Towards Zero-shot Word Image Recognition

arXiv: https://arxiv.org/abs/2105.15093 · HTML: https://mlsanity.com/paper/2105.15093/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 13 of 13)

- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Zero-Shot Learning -- The Good, the Bad and the Ugly](https://mlsanity.com/paper/1703.04394/) (2017)
- [Label-Embedding for Image Classification](https://mlsanity.com/paper/1503.08677/) (2015)
- [Scene Text Detection and Recognition: The Deep Learning Era](https://mlsanity.com/paper/1811.04256/) (2018)
- [Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective](https://mlsanity.com/paper/1909.05995/) (2019)
- [Preserving Semantic Relations for Zero-Shot Learning](https://mlsanity.com/paper/1803.03049/) (2018)
- [Semantically Aligned Bias Reducing Zero Shot Learning](https://mlsanity.com/paper/1904.07659/) (2019)
- [PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents](https://mlsanity.com/paper/1604.00187/) (2016)
- [A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages](https://mlsanity.com/paper/1912.10016/) (2019)
- [HWNet v2: An Efficient Word Image Representation for Handwritten Documents](https://mlsanity.com/paper/1802.06194/) (2018)
- [Zero-Shot Kernel Learning](https://mlsanity.com/paper/1802.01279/) (2018)
- [Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting in Handwritten Manuscript Collections](https://mlsanity.com/paper/1703.07645/) (2017)
- [Annotation-free Learning of Deep Representations for Word Spotting using Synthetic Data and Self Labeling](https://mlsanity.com/paper/2003.01989/) (2020)

## Built on by (most cited first, 2 of 2)

- [Ancient Script Image Recognition and Processing: A Review](https://mlsanity.com/paper/2506.19208/) (2025)
- [A statistical approach to bias in zero-shot learning: the lens of handwriting recognition](https://mlsanity.com/paper/2609.10084/) (2026)

## Abstract (arXiv)

Annotating words in a historical document image archive for word image recognition purpose demands time and skilled human resource (like historians, paleographers). In a real-life scenario, obtaining sample images for all possible words is also not feasible. However, Zero-shot learning methods could aptly be used to recognize unseen/out-of-lexicon words in such historical document images. Based on previous state-of-the-art method for zero-shot word recognition Pho(SC)Net, we propose a hybrid model based on the CTC framework (Pho(SC)-CTC) that takes advantage of the rich features learned by Pho(SC)Net followed by a connectionist temporal classification (CTC) framework to perform the final classification. Encouraging results were obtained on two publicly available historical document datasets and one synthetic handwritten dataset, which justifies the efficacy of Pho(SC)-CTC and Pho(SC)Net.