Ancient Near Eastern cuneiform tablets document one of the earliest writing systems known. Extensive collections of cuneiform tablets are in the process of being digitized by many institutions worldwide in an effort to make the digitized inscriptions available to remote researchers. With an increasing demand for digitization in the museum sector, this project addresses the ineffectiveness of manual image processing and aims to contribute to ancient Near Eastern studies with automated image processing of cuneiform tablets.
Manually, a six-sided tablet is scanned on each side on a flatbed scanner and sent for post-production, in which the images are digitally enhanced and stitched in the form of a “fatcross” using programs like Photoshop. Automation of such image processing will include 1) preparation of the image data, 2) image segmentation with methods like thresholding, and 3) accurate assembly of separate images. This research aims to identify a simple and effective edge detection method and its implementation parameters to automate building a fatcross.
Cuneiform tablets of the ancient Near East document one of the earliest writing systems known. Millions of such cuneiform tablets are archived and are beginning to be digitized by many collections around the world. The six-sided tablet is conventionally digitized by scanning each sides on a flat-bed scanner and stitching the processed scans in the form of a `fatcross’. With the increasing demand for digitization, an effective means of fatcross automation is in demand to tackle the time-consuming process of manual digitization. A challenge in implementing an automated fatcross production is identifying appropriate image segmentation methods. Edge detection is a viable method given its sensitivity to pixel intensity change, such as that of the rapid transition between an illuminated tablet and its dark background. This research aims to identify the most appropriate edge detection method and its implementation parameters to detect the edges from a cuneiform tablet scan.
1) Sentiment Analysis on Popular Fiction Books (Data Science)
Research topic/question:
People have long discussed “formulas” for writing successful narrative fiction, such as the three-act structure or the hero’s journey, both models that follow a certain plot formula. I am interested in finding out whether a sentiment progression throughout a fiction book has a relationship to the popularity of the book.
Technology/data:
Python, pandas, Matplotlib. Sentiment analysis. Corpus from Project Gutenbeg.
General approach/method:
To find out whether there is a consistent pattern of sentiment, I will perform a sentiment analysis on each book, either by chapters, by n number of words (e.g. every 1000 words), or by n percentages (e.g. every 10% of the book). I will compare n books of high popularity, n books of medium popularity, and n books of low popularity, to observe whether certain patterns of sentiment correlates to popularity.
Difficulties/potential problems:
It will be difficult to quantify ‘popularity’. If determined by the number of books sold, an ancient book may have significantly more copies sold then a contemporary book of equal popularity. To select a corpus with reduced bias, I will consider limiting selections by a single genre, single author, and/or time (consider only books published between 1800-1900).
One other concern is that this project may yield no correlation.
2) COVID-19 in South Korea (Data Science)
Research topic/question:
Understanding COVID-19 in South Korea by approaching the comprehensive dataset with foundational questions, such as “How is the virus distributed among people of different age?”, “Which regions were most impacted by the virus?”, “Which specific location (certain bar, church) has been central in spreading the virus?”, “What pre-existing conditions have been the most detrimental to COVID patients?”, “Can the future number of cases be predicted through this data?”, “How does search trend relate to the magnitude of reported cases?”.
How can I use more creative methods to approach the data? Would it be possible to provide new insights when so many data analysis has been performed around the world already? Should I use a worldwide dataset, instead of one country?
3) Edge Detection for Scanned Images of Cuneiform Tablets (Machine Learning)
Research topic/question:
In collections that preserve ancient Mesopotamian cuneiform tablets, the tablets are digitized and uploaded to the database by scanning each edge of a tablet then combining each image into the shape of a ‘fat cross’, like a rectangular box net. This digitization of thousands of tablets is still done manually in some institutions (which was my internship last summer at the University of Chicago). I am interested in creating a specific edge detection algorithm that, when given a collection of tablet scans, correctly detects the edge of the tablet. Through research and experimentation/implementation, I will decide which edge detection algorithm is the most suitable for tablet scans.
Raw images of cuneiform tablet scans (The Oriental Institute at the University of Chicago has made its digital collection available for the public. I can request high definition images for research here).
Difficulties/potential problems:
Evaluation metric: The edge detection method will need an evaluation metric. When processing manually, the standard is to leave a narrow border around the detected edge to prevent abrupt endings that potentially cut off inscriptions at the sides (see images below). I will have to quantify this.
How the edge should be cropped.
Data: I will reach out to my previous supervisor at UChicago for more images of tablet scans. I can also reach out to a project manager at CDLI for images. However, there is a possibility they may not respond in time for the project.
Further possibilities, beyond this project:
Once I have the edge detection algorithm, I can further the project to completely automate the process of creating a ‘fat cross’ image. I can also create a web interface, which enables the user to dump a large number of tablet scans and retrieve a series of completed ‘fat crosses’.
