CuneiML

This repository hosts the code to get the artifects of Cuneiform in the paper CuneiML: A Cuneiform Dataset for Machine Learning.

@article{Chen-2023,
 author = {Chen, Danlu and Agarwal, Aditi and Berg-Kirkpatrick, Taylor and Myerston, Jacobo},
 doi = {10.5334/johd.151},
 journal = {Journal of Open Humanities Data},
 month = {Dec},
 title = {CuneiML: A Cuneiform Dataset for Machine Learning},
 year = {2023}
}

Data

We provoide the dehygration version of the data in CuneiML_V1.2.json:

• Cutouts (image): major face cutouts (we provide the bounding boxes only, and users need to obtain the orignal images on their own because of copyright)
• Unicode (text): Cuneiform in Unicode

Additionally, we provide the transliteration data we obtained from CDLI.

• Transliteration (text): Cuneiform Transliteration (downloaded from CDLI)

The file iid_split.json provides the CDLI ID of train/valid/test split for the time period classification experiment.

---

the CuneiML_V1.2.json is a list of dict as below:

{
 'id': 131837,
 'img_url': 'https://cdli.mpiwg-berlin.mpg.de/dl/photo/P131837.jpg',    # link to photo
 'lineart': 'https://cdli.mpiwg-berlin.mpg.de/dl/lineart/P131837_l.jpg',# link to lineart
 # the bounding box, [[x_1, y_1], [x_2, y_2]]
 # where (x_1, y_1) is the left upper vertex and (x_2, y_2) is the lower right vertex of the bounding box of the cutout
 'bboxes': [[204.0, 200.0], [523.0, 522.0]],                            
 'text': {
   'obverse': [
      {'raw': '2(gesz2) 4(asz) 2(barig) 4(disz) sila3 gur',
         'num': '1',
         'sign': ['𒐂', '𒐉', '𒋡', '𒄥']},
      {'raw': 'a2 lu2 hun-ga2', 
         'num': '2', 
         'sign': ['𒀉', '𒇽', '𒂠', '𒂷']},
      {'raw': 'ugu2 lu2- <D> inanna ba-a-gar',
         'num': '3',
         'sign': ['𒀀𒅗', '𒇽', '<D>', '𒈹', '𒁀', '𒀀', '𒃻']}],
   'reverse': [
      {'raw': 'mu sza-asz-ru ki  ba-hul',
       'num': '1',
       'sign': ['𒈬', '𒊭', '𒀸', '𒊒', '𒆠', '𒁀', '𒅆𒌨']}]
   }
'geo': 'Umma (mod. Tell Jokha)',
'time': 'Ur III (ca. 2100-2000 BC)',
'genre': 'Administrative',
}

Note that around 1% of the cuneiform Unicode is not convert automatically.

We stored the text by their faces (i.e. observe, reserve, left, right, ...). The raw field is the transliteration obtained from CDLI and the sign field is the Cuneiform Unicode of the lines. The num field is from CDLI's line label. To collaspe the data into pure text of Unicode or transliteration, here is the example to get pure text of tablet 131837:

import json
data = json.load("./CuneiML_V1.2.json")
CDLI_id = 131837 
unicode = []
transliteration = []
for face in data[CDLI_id]['text']:
   for line in data[CDLI_id]['text'][face]:
      if 'raw' in line:
         transliteration.append(line['raw'])
      else:
         transliteration.append('<B>') # broken line
      if 'sign' in line:
         unicode.append(line['sign'])
      else:
         unicode.append('<B>')

Getting the cutouts

1. Downlad the images on your own the photographs of each table from CDLI. P100001.jpg is the photograph of Tablet id=100001. For example:

import requests

def download_image(url, filename):
    response = requests.get(url)
    if response.status_code == 200:
        with open(filename, 'wb') as file:
            file.write(response.content)
        print(f"Image downloaded successfully: {filename}")
    else:
        print(f"Failed to download image. Status code: {response.status_code}")

download_image(url="https://cdli.mpiwg-berlin.mpg.de/dl/photo/P131837.jpg", filename="P131837.jpg")

2. Cut the image using the PIL package using the bounding box:

    from PIL import Image
    im = Image.open("P131837.jpg")
    # the crop command requires a 4-item tuple (x1, y1, x2, y2) to indicate the bounding box for the cropping operation. 
    im.crop((204.0, 200.0, 523.0, 522.0)).save("P131837_cutout.jpg")

Code

We also release the code to get the cutouts/unicode for new data that not includes in the current selection of dataset.

get_cutouts: the code to get major face cutouts. Please refer to get_cutouts/README.md for details.

cuneiform_unicode: the code to convert transliteration (in ATF) to cuneiform unicode.

Getting cutouts

(WIP)

Getting Unicode for other transliteration

We also provide a script to convert any transliteration into Unicode.

run cd cuneiform_unicode; python main.py --raw_text "1. 4(disz) gu4 niga \n 2. _mu us2-sa {kusz}a2-la2 e2 {d}nanna-ra a mu-na-ru" and you can get the unicode given the transliteration string.

Note that the code primary design for parsing CDLI's ATF and therefore a leading line number is reqiured for the raw_text. Multiple lines are seperated by \n.

Change logs

2024.02.21 Update image urls, more instruction