Leveraging Clustering for Document Layout Analysis in Machine Learning Projects

Introduction In machine learning / AI projects, dealing with diverse document layouts can pose a significant challenge. However, by leveraging clustering techniques, we can identify documents with similar layouts and selectively augment the training data to improve model performance. In this blog post, we will explore the concept of document layout analysis, the importance of targeted data augmentation, and how clustering can aid in identifying underperforming documents for augmentation. Document Layout Analysis Document layout analysis involves understanding the structure and organization of different types of documents. It plays a crucial role in tasks such as optical character recognition (OCR), information extraction, and document classification. Variation in document layouts can pose difficulties when training machine learning models, as they may require different preprocessing for feature extraction techniques. Utilizing Clustering for Document Layout Analysis Clustering techniques provide a valuable approach for grouping similar documents based on their layout characteristics. These techniques enable us to identify clusters of documents with similar structures, formatting, or visual features. By applying clustering algorithms to the existing dataset, we can automatically group documents into distinct clusters, each representing a specific layout type. <a href="https://medium.com/@nikhilkumar.marepally/leveraging-clustering-for-document-layout-analysis-in-machine-learning-projects-f4b8db9e36a3">Visit Now</a>