# End-to-End Approach for Recognition of Historical Digit Strings

arXiv: https://arxiv.org/abs/2104.13666 · HTML: https://mlsanity.com/paper/2104.13666/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Learning Structured Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1608.03665/) (2016)
- [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)
- [Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval](https://mlsanity.com/paper/1604.04994/) (2016)
- [Attend, Copy, Parse -- End-to-end information extraction from documents](https://mlsanity.com/paper/1812.07248/) (2018)
- [Towards Data-efficient Modeling for Wake Word Spotting](https://mlsanity.com/paper/2010.06659/) (2020)
- [A Comprehensive Comparison of End-to-End Approaches for Handwritten Digit String Recognition](https://mlsanity.com/paper/2010.15904/) (2020)
- [Deep Spatial Pyramid: The Devil is Once Again in the Details](https://mlsanity.com/paper/1504.05277/) (2015)
- [DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers](https://mlsanity.com/paper/1606.04702/) (2016)
- [An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings](https://mlsanity.com/paper/2004.03337/) (2020)

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

- [Evaluation of Different Annotation Strategies for Deployment of Parking Spaces Classification Systems](https://mlsanity.com/paper/2207.11372/) (2022)

## Abstract (arXiv)

The plethora of digitalised historical document datasets released in recent years has rekindled interest in advancing the field of handwriting pattern recognition. In the same vein, a recently published data set, known as ARDIS, presents handwritten digits manually cropped from 15.000 scanned documents of Swedish church books and exhibiting various handwriting styles. To this end, we propose an end-to-end segmentation-free deep learning approach to handle this challenging ancient handwriting style of dates present in the ARDIS dataset (4-digits long strings). We show that with slight modifications in the VGG-16 deep model, the framework can achieve a recognition rate of 93.2%, resulting in a feasible solution free of heuristic methods, segmentation, and fusion methods. Moreover, the proposed approach outperforms the well-known CRNN method (a model widely applied in handwriting recognition tasks).