Inaccurate Bounding Boxes in "wildreceipt" Dataset

### Describe the bug

I would like to bring to your attention an issue related to the accuracy of bounding boxes within the "wildreceipt" dataset, which is made available through the Hugging Face API. Specifically, I have identified a discrepancy between the bounding boxes generated by the dataset loading commands, namely `load_dataset("Theivaprakasham/wildreceipt")` and `load_dataset("jinhybr/WildReceipt")`, and the actual labels and corresponding bounding boxes present in the dataset.

To illustrate this divergence, I've provided two examples in the form of screenshots. These screenshots highlight the contrasting outcomes between my personal implementation of the dataloader and the implementation offered by Hugging Face:

**Example 1:**

![image](https://github.com/huggingface/datasets/assets/50714796/7a6604d2-899d-4102-a008-1a28c90698f1)
![image](https://github.com/huggingface/datasets/assets/50714796/eba458c7-d3af-4868-a520-8b683aa96f66)
![image](https://github.com/huggingface/datasets/assets/50714796/9f394891-5f5b-46f7-8e52-071b724aedab)

**Example 2:**

![image](https://github.com/huggingface/datasets/assets/50714796/a2b2a8d3-124e-4990-b64a-5133cf4be2fe)
![image](https://github.com/huggingface/datasets/assets/50714796/6ee25642-35aa-40ad-ac1e-899d33be90df)
![image](https://github.com/huggingface/datasets/assets/50714796/5e42ff91-9fc4-4520-8803-0e225656f96c)

It's important to note that my dataloader implementation is based on the same dataset files as utilized in the Hugging Face implementation. For your reference, you can access the dataset files through this link: [wildreceipt dataset files](https://download.openmmlab.com/mmocr/data/wildreceipt.tar).

This inconsistency in bounding box accuracy warrants investigation and rectification for maintaining the integrity of the "wildreceipt" dataset. Your attention and assistance in addressing this matter would be greatly appreciated.

### Steps to reproduce the bug

```python
import matplotlib.pyplot as plt
from datasets import load_dataset


# Define functions to convert bounding box formats
def convert_format1(box):
    x, y, w, h = box
    x2, y2 = x + w, y + h
    return [x, y, x2, y2]


def convert_format2(box):
    x1, y1, x2, y2 = box
    return [x1, y1, x2, y2]


def plot_cropped_image(image, box, title):
    cropped_image = image.crop(box)
    plt.imshow(cropped_image)
    plt.title(title)
    plt.axis('off')
    plt.savefig(title+'.png')
    plt.show()


doc_index = 1
word_index = 3
dataset = load_dataset("Theivaprakasham/wildreceipt")['train']

bbox_hugging_face = dataset[doc_index]['bboxes'][word_index]
text_unit_face = dataset[doc_index]['words'][word_index]

common_box_hugface_1 = convert_format1(bbox_hugging_face)
common_box_hugface_2 = convert_format2(bbox_hugging_face)

plot_cropped_image(image_hugging, common_box_hugface_1,
                   f'Hugging Face Bouding boxes (x,y,w,h format) \n its associated text unit: {text_unit_face}')
plot_cropped_image(image_hugging, common_box_hugface_2,
                   f'Hugging Face Bouding boxes (x1,y1,x2, y2 format) \n its associated text unit: {text_unit_face}')
```


### Expected behavior

The bounding boxes generated by the "wildreceipt" dataset in HuggingFace implementation loading commands should accurately match the actual labels and bounding boxes of the dataset.


### Environment info

- Python version: 3.8
- Hugging Face datasets version: 2.14.2
- Dataset file taken from this link: https://download.openmmlab.com/mmocr/data/wildreceipt.tar