Comparisons in End-to-End Pipeline Designs for Customized Document Information Extraction

As businesses continue to adapt to the shift toward the digitalization of corporate tasks, one particular remaining financial and temporal bottleneck is the need for manual labor in interpreting digital documents and recording relevant information. Much work and research has been done, utilizing both machine learning techniques and traditional algorithmic approaches, to alleviate the resources required for this task by developing automated solutions for extracting information from such documents. However, current commercially available solutions typically struggle with either generalization to unique document structures or with handling the range of potential details present within a document type. The thesis introduces and compares two distinct end-to-end pipeline architectures combining neural networks with algorithmic techniques to effectively extract custom key-value information, with one focusing on commercial invoices with consistent keys and the other on technical specification sheets with variable keys. With accuracy, generalizability, and modularity as priorities, their use cases, benefits, and limitations are explored alongside comparisons with existing commercial solutions.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Kim, Seok Hyeon

Advisor dc:contributor.advisor

• Gupta, Amar

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/153889

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/153889