class_docstr: IIITHWS dataset from `"Generating Synthetic Data for Text Recognition"
  <https://arxiv.org/pdf/1608.04224.pdf>`_ | `"repository" <https://github.com/kris314/hwnet>`_ |
  `"website" <https://cvit.iiit.ac.in/research/projects/cvit-projects/matchdocimgs>`_.
  
  >>> # NOTE: This is a pure recognition dataset without bounding box labels.
  >>> # NOTE: You need to download the dataset.
  >>> from doctr.datasets import IIITHWS
  >>> train_set = IIITHWS(img_folder="/path/to/iiit-hws/Images_90K_Normalized",
  >>>                     label_path="/path/to/IIIT-HWS-90K.txt",
  >>>                     train=True)
  >>> img, target = train_set[0]
  >>> test_set = IIITHWS(img_folder="/path/to/iiit-hws/Images_90K_Normalized",
  >>>                    label_path="/path/to/IIIT-HWS-90K.txt")
  >>>                    train=False)
  >>> img, target = test_set[0]
  
  Args:
  ----
      img_folder: folder with all the images of the dataset
      label_path: path to the file with the labels
      train: whether the subset should be the training one
      **kwargs: keyword arguments from `AbstractDataset`.
code: def extra_repr(self) -> str:
      return f"train={self.train}"
  
file_path: doctr/doctr/datasets/iiithws.py
imports: import os
  from random import sample
  from typing import Any, List, Tuple
  from tqdm import tqdm
  from .datasets import AbstractDataset
label_desc: Use image node_id 2 for calling the IIITHWS obj's underlying member method code with example usage: obj.extra_repr() and returns: str, self