Document Understanding Insight #2

Challenge 2: Refining Document Classification

Problem Statement: In an effort to enhance the capabilities of our document processing workflow, we introduced additional layout samples into the Intelligent Keyword Classifier with the objective of improving document classification. Despite these efforts, the results were underwhelming, as the classifier struggled to accurately categorize documents based on their layouts. Particularly disconcerting was its consistent failure to correctly identify documents with newly introduced layouts, often confusing them with similar, existing ones.

Intuition: Upon closer examination of the classification outcomes, a pattern emerged suggesting the classifier’s performance was directly tied to the volume of samples available per layout. It became evident that layouts represented by a larger corpus of samples were classified with a higher degree of accuracy, while those with fewer examples, especially if they bore visual similarities to other layouts, were prone to misclassification. This discrepancy led us to hypothesize that the classifier’s algorithm was heavily reliant on sample size as a determinant of classification confidence, thereby disadvantaging less-represented layouts.

Solution: Addressing this challenge required a two-pronged strategy. First, we made the decision to consolidate the frequently misclassified layouts into a unified ‘misclassified’ category. This innovative approach allowed us to bypass the immediate problem of distinguishing between visually similar layouts with insufficient examples. Second, we embarked on a meticulous process of sample redistribution, aiming to achieve a more balanced representation across all categories. By reducing the total number of classification categories and ensuring that each layout was supported by a proportionate number of samples, we significantly boosted the classifier’s ability to make accurate distinctions. This targeted intervention not only improved the classification success rate but also streamlined the overall efficiency of our document processing system, demonstrating the crucial role of strategic sample management in the optimization of machine learning classifiers.