# Improving Amharic Handwritten Word Recognition Using Auxiliary Task

arXiv: https://arxiv.org/abs/2202.12687 · HTML: https://mlsanity.com/paper/2202.12687/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition](https://mlsanity.com/paper/1406.2227/) (2014)
- [Reading Scene Text in Deep Convolutional Sequences](https://mlsanity.com/paper/1506.04395/) (2015)
- [Boosting Handwriting Text Recognition in Small Databases with Transfer Learning](https://mlsanity.com/paper/1804.01527/) (2018)
- [Handwritten Amharic Character Recognition Using a Convolutional Neural Network](https://mlsanity.com/paper/1909.12943/) (2019)
- [Multi-script Handwritten Digit Recognition Using Multi-task Learning](https://mlsanity.com/paper/2106.08267/) (2021)

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

- [Offline Handwritten Amharic Character Recognition Using Few-shot Learning](https://mlsanity.com/paper/2210.00275/) (2022)

## Abstract (arXiv)

Amharic is one of the official languages of the Federal Democratic Republic of Ethiopia. It is one of the languages that use an Ethiopic script which is derived from Gee'z, ancient and currently a liturgical language. Amharic is also one of the most widely used literature-rich languages of Ethiopia. There are very limited innovative and customized research works in Amharic optical character recognition (OCR) in general and Amharic handwritten text recognition in particular. In this study, Amharic handwritten word recognition will be investigated. State-of-the-art deep learning techniques including convolutional neural networks together with recurrent neural networks and connectionist temporal classification (CTC) loss were used to make the recognition in an end-to-end fashion. More importantly, an innovative way of complementing the loss function using the auxiliary task from the row-wise similarities of the Amharic alphabet was tested to show a significant recognition improvement over a baseline method. Such findings will promote innovative problem-specific solutions as well as will open insight to a generalized solution that emerges from problem-specific domains.