# End-to-End Text Recognition with Hybrid HMM Maxout Models

arXiv: https://arxiv.org/abs/1310.1811 · HTML: https://mlsanity.com/paper/1310.1811/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2013.

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

- [Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study](https://mlsanity.com/paper/2112.15093/) (2021)
- [Convolutional Neural Networks with Gated Recurrent Connections](https://mlsanity.com/paper/2106.02859/) (2021)
- [Extending TrOCR for Text Localization-Free OCR of Full-Page Scanned Receipt Images](https://mlsanity.com/paper/2212.05525/) (2022)
- [Vision-Language Adaptive Mutual Decoder for OOV-STR](https://mlsanity.com/paper/2209.00859/) (2022)

## Abstract (arXiv)

The problem of detecting and recognizing text in natural scenes has proved to be more challenging than its counterpart in documents, with most of the previous work focusing on a single part of the problem. In this work, we propose new solutions to the character and word recognition problems and then show how to combine these solutions in an end-to-end text-recognition system. We do so by leveraging the recently introduced Maxout networks along with hybrid HMM models that have proven useful for voice recognition. Using these elements, we build a tunable and highly accurate recognition system that beats state-of-the-art results on all the sub-problems for both the ICDAR 2003 and SVT benchmark datasets.