Introduction & Motivation

Deliverables

39,587 records related to postcards with image copyrights, along with their metadata, from the Europeana website.
OCR results of a sample set of 350 images containing text.
GPT-3.5 prediction results for a sample set of 350 images containing text, based on OCR results.
A high-quality, manually annotated Ground Truth for a sample set of 309 images.
GPT-3.5 prediction results for Ground Truth.
GPT-4 prediction results for Ground Truth.
An interactive webpage displaying the mapping of the postcards.
The GitHub repository contains all the codes for the whole project.

Timeframe	Task	Completion
Week 4	Explore postcard search results on Europeana's website Study the Europeana API documentation and get an access key. Extract data of postcards using the Europeana API	✅
Week 5	Clean data using metadata. Analyze the data of Europeana postcards Prepare sample image sets and explore prediction methods	✅
Week 6	Decide to focus on postcards with text Test and evaluate the effectiveness of multiple OCR models	✅
Week 7	Use OCR and NER for prediction Test and evaluate the effectiveness of multiple NER tools Explore alternative forecasting methods	✅
Week 8	Introduce ChatGPT for the prediction(OCR+GPT-3.5+NER) Try to make predictions directly using GPT-4	✅
Week 9	Optimize GPT-3.5 prompt for better results Compare the results of OCR + GPT-3.5 (optimized prompts) to those of GPT-4.	✅
Week 10	Complete the pipeline for the entire prediction process Prepare a sample set to evaluate the effect	✅
Week 11	Explore the visualization methods Refine the test set and analyze it	✅
Week 12	Use the TA's annotation tool for building a ground truth Build the visualization platform	✅
Week 13	Testing and refinement of the Web application Analyze the results of the test set evaluation	✅
Week 14	Prepare the final report and presentation	✅