# LeaData a novel reference data of leather images for automatic species identification

## TL;DR

This paper introduces LeaData, a large leather image dataset for automatic species identification using digital microscopes.

## Contribution

The novel LeaData dataset provides diverse leather images for accurate species identification using grain surface analysis.

## Key findings

- LeaData contains 38,172 images of four species from 137 leather samples.
- The dataset includes both ideal and non-ideal grain patterns from different body parts.
- The goal is to enable smart species identification for leather specialists and manufacturers.

## Abstract

In the leather industry, the mammalian skins of buffalo, cow, goat, and sheep are the permissible materials for leather-making. They serve the trade of quality leather products; hence, the knowledge of animal species in leather is inevitable. The traditional identification techniques are prone to ambiguous predictions due to insufficient reference studies. Indeed, leather image analysis with big data can pave the way for automatic and objective analysis with accurate prediction. This study focuses on creating novel and unique leather image data, LeaData. The objective is to automatically determine species from grain surface analysis. Hence, it employs a simple, cheaper, handheld digital microscope for leather image acquisition. The magnifying parameter 47\documentclass[12pt]{minimal}
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