# Tifinagh-IRCAM Handwritten character recognition using Deep learning

arXiv: https://arxiv.org/abs/1912.10338 · HTML: https://mlsanity.com/paper/1912.10338/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2019.

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

- [Optical Character Recognition and Transcription of Berber Signs from Images in a Low-Resource Language Amazigh](https://mlsanity.com/paper/2303.13549/) (2023)

## Abstract (arXiv)

In this paper, we exploit the benefits of the deep learning approach to design an efficient system of Amazigh handwritten recognition. Indeed, this approach has proved a greater efficiency in the various domains, especially recognition tasks. However, to take full advantage of this approach it's necessary to construct an adequate dataset of training and testing that represent faithfully the concerned problem. To this end, we have prepared our dataset of 102 writers each one contains 33 characters of IRCAM-Tifinagh. Inspired by the MNIST database, the set of characters is size-normalized and centered in a fixed-size image. The resulting is a grey level image of size 28x28, where the black color is the non-color of the character. The number of images produced after this preprocessing step is 3,366.