# TransDocs: Optical Character Recognition with word to word translation

arXiv: https://arxiv.org/abs/2304.07637 · HTML: https://mlsanity.com/paper/2304.07637/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 15 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis](https://mlsanity.com/paper/1904.01906/) (2019)
- [Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold](https://mlsanity.com/paper/1812.02592/) (2018)
- [Dynamic Object Removal for Effective Slam](https://mlsanity.com/paper/2303.10923/) (2023)
- [KeyDetect --Detection of anomalies and user based on Keystroke Dynamics](https://mlsanity.com/paper/2304.03958/) (2023)
- [Learning video embedding space with Natural Language Supervision](https://mlsanity.com/paper/2303.14584/) (2023)

## Abstract (arXiv)

While OCR has been used in various applications, its output is not always accurate, leading to misfit words. This research work focuses on improving the optical character recognition (OCR) with ML techniques with integration of OCR with long short-term memory (LSTM) based sequence to sequence deep learning models to perform document translation. This work is based on ANKI dataset for English to Spanish translation. In this work, I have shown comparative study for pre-trained OCR while using deep learning model using LSTM-based seq2seq architecture with attention for machine translation. End-to-end performance of the model has been expressed in BLEU-4 score. This research paper is aimed at researchers and practitioners interested in OCR and its applications in document translation.