# Handwritten digit string recognition by combination of residual network and RNN-CTC

arXiv: https://arxiv.org/abs/1710.03112 · HTML: https://mlsanity.com/paper/1710.03112/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs](https://mlsanity.com/paper/2509.11662/) (2025)
- [Data Metabolism: An Efficient Data Design Schema For Vision Language Model](https://mlsanity.com/paper/2504.12316/) (2025)
- [An approach to extract information from academic transcripts of HUST](https://mlsanity.com/paper/2304.11454/) (2023)
- [Capybara-OMNI: An Efficient Paradigm for Building Omni-Modal Language Models](https://mlsanity.com/paper/2504.12315/) (2025)

## Abstract (arXiv)

Recurrent neural network (RNN) and connectionist temporal classification (CTC) have showed successes in many sequence labeling tasks with the strong ability of dealing with the problems where the alignment between the inputs and the target labels is unknown. Residual network is a new structure of convolutional neural network and works well in various computer vision tasks. In this paper, we take advantage of the architectures mentioned above to create a new network for handwritten digit string recognition. First we design a residual network to extract features from input images, then we employ a RNN to model the contextual information within feature sequences and predict recognition results. At the top of this network, a standard CTC is applied to calculate the loss and yield the final results. These three parts compose an end-to-end trainable network. The proposed new architecture achieves the highest performances on ORAND-CAR-A and ORAND-CAR-B with recognition rates 89.75% and 91.14%, respectively. In addition, the experiments on a generated captcha dataset which has much longer string length show the potential of the proposed network to handle long strings.