---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': assamese
          '1': bangla
  splits:
  - name: train
    num_bytes: 3195764.0
    num_examples: 700
  - name: test
    num_bytes: 1340803.0
    num_examples: 300
  download_size: 4300152
  dataset_size: 4536567.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

## Overview

The dataset is composed of 1000 images containing Bangla and Assamese text. 
Bangla and Assamese are closely related languages with sharing the 
Bengali–Assamese script and have similar lexical 
constructs (https://en.wikipedia.org/wiki/Bengali%E2%80%93Assamese_script#cite_note-MajR-22).
The text is this dataset has been generated from Open English Bible 
(https://openenglishbible.org/oeb/2022.1/OEB-2022.1-US.txt) by:
- Selecting the first 500 lines containg between 50 and 100 chars (after 
stripping leading / trailing spaces)
- Translating them to Bangla and Assamese using Google Translate API.
- Dropping words containing the following characters: {"ৰ", "র", "ৱ", 
"অʼ", "অ্যা", "এ্যা", "এʼ", "’", "1", "2", "3", "4", "5", "6", "7", "8", "9", 
"০", "১", "২", "৩", "৪", "৫", "৬", "৭", "৮", "৯"} as these characters are:
-- Either specific to Bangla or Assamese
-- Or inconsistenly translated by Google Translate API
- Transcibing the text to 224*224 PNG images using the 
NotoSansBengali-Regular.ttf font (https://cdn.jsdelivr.net/gh/notofonts/notofonts.github.io/fonts/NotoSansBengali/unhinted/ttf/NotoSansBengali-Regular.ttf)

## Dataset Summary

- **Dataset Name**: anrikus/lexical_diff_bangla_assamese_v2
- **Dataset Type**: Images
- **Images Format**: 224*224 PNG images
- Number of Instances**:
  - **Train**: 700 [350 Bangla + 350 Assamese]
  - **Test**: 300 [150 Bangla + 150 Assamese]
- **Number of Labels/Classes**: 2
- **Languages**: Bangla / Assamese

## Usage

### Installation

Explain how to install and load the dataset using the Hugging Face `datasets` library.

```python
from datasets import load_dataset

dataset = load_dataset("anrikus/lexical_diff_bangla_assamese_v2")
```