# Handwriting Recognition with Novelty

arXiv: https://arxiv.org/abs/2105.06582 · HTML: https://mlsanity.com/paper/2105.06582/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [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)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [The Extreme Value Machine](https://mlsanity.com/paper/1506.06112/) (2015)
- [Sparse Representation-based Open Set Recognition](https://mlsanity.com/paper/1705.02431/) (2017)
- [A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis](https://mlsanity.com/paper/1905.09113/) (2019)
- [A Unifying Framework for Formal Theories of Novelty:Framework, Examples and Discussion](https://mlsanity.com/paper/2012.04226/) (2020)

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

- [Exploring the Open World Using Incremental Extreme Value Machines](https://mlsanity.com/paper/2205.14892/) (2022)

## Abstract (arXiv)

This paper introduces an agent-centric approach to handle novelty in the visual recognition domain of handwriting recognition (HWR). An ideal transcription agent would rival or surpass human perception, being able to recognize known and new characters in an image, and detect any stylistic changes that may occur within or across documents. A key confound is the presence of novelty, which has continued to stymie even the best machine learning-based algorithms for these tasks. In handwritten documents, novelty can be a change in writer, character attributes, writing attributes, or overall document appearance, among other things. Instead of looking at each aspect independently, we suggest that an integrated agent that can process known characters and novelties simultaneously is a better strategy. This paper formalizes the domain of handwriting recognition with novelty, describes a baseline agent, introduces an evaluation protocol with benchmark data, and provides experimentation to set the state-of-the-art. Results show feasibility for the agent-centric approach, but more work is needed to approach human-levels of reading ability, giving the HWR community a formal basis to build upon as they solve this challenging problem.