Unveiling Document Structures with YOLOv5 Layout Detection
Herman Sugiharto Yorissa Silviana Yani Siti Nurpazrin
The current digital environment is characterized by the widespread presence
of data, particularly unstructured data, which poses many issues in sectors
including finance, healthcare, and education. Conventional techniques for data
extraction encounter difficulties in dealing with the inherent variety and
complexity of unstructured data, hence requiring the adoption of more efficient
methodologies. This research investigates the utilization of YOLOv5, a
cutting-edge computer vision model, for the purpose of rapidly identifying
document layouts and extracting unstructured data.
  The present study establishes a conceptual framework for delineating the
notion of "objects" as they pertain to documents, incorporating various
elements such as paragraphs, tables, photos, and other constituent parts. The
main objective is to create an autonomous system that can effectively recognize
document layouts and extract unstructured data, hence improving the
effectiveness of data extraction.
  In the conducted examination, the YOLOv5 model exhibits notable effectiveness
in the task of document layout identification, attaining a high accuracy rate
along with a precision value of 0.91, a recall value of 0.971, an F1-score of
0.939, and an area under the receiver operating characteristic curve (AUC-ROC)
of 0.975. The remarkable performance of this system optimizes the process of
extracting textual and tabular data from document images. Its prospective
applications are not limited to document analysis but can encompass
unstructured data from diverse sources, such as audio data.
  This study lays the foundation for future investigations into the wider
applicability of YOLOv5 in managing various types of unstructured data,
offering potential for novel applications across multiple domains.