# An End-to-End Khmer Optical Character Recognition using Sequence-to-Sequence with Attention

arXiv: https://arxiv.org/abs/2106.10875 · HTML: https://mlsanity.com/paper/2106.10875/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)](https://mlsanity.com/paper/2001.00139/) (2020)
- [End-to-End Optical Character Recognition for Bengali Handwritten Words](https://mlsanity.com/paper/2105.04020/) (2021)
- [Joint Khmer Word Segmentation and Part-of-Speech Tagging Using Deep Learning](https://mlsanity.com/paper/2103.16801/) (2021)
- [Sequence to Sequence Learning for Optical Character Recognition](https://mlsanity.com/paper/1511.04176/) (2015)
- [On the Accuracy of CRNNs for Line-Based OCR: A Multi-Parameter Evaluation](https://mlsanity.com/paper/2008.02777/) (2020)

## Abstract (arXiv)

This paper presents an end-to-end deep convolutional recurrent neural network solution for Khmer optical character recognition (OCR) task. The proposed solution uses a sequence-to-sequence (Seq2Seq) architecture with attention mechanism. The encoder extracts visual features from an input text-line image via layers of residual convolutional blocks and a layer of gated recurrent units (GRU). The features are encoded in a single context vector and a sequence of hidden states which are fed to the decoder for decoding one character at a time until a special end-of-sentence (EOS) token is reached. The attention mechanism allows the decoder network to adaptively select parts of the input image while predicting a target character. The Seq2Seq Khmer OCR network was trained on a large collection of computer-generated text-line images for seven common Khmer fonts. The proposed model's performance outperformed the state-of-art Tesseract OCR engine for Khmer language on the 3000-images test set by achieving a character error rate (CER) of 1% vs 3%.