Enhancing Classification of Low-Quality Text Documents through Multimodal Approach

Traditional document classification relies on text-based Natural Language Processing (NLP), limiting its effectiveness with visually complex or Optical Character Recognition (OCR)-sensitive documents. Indeed, the result of text recognition from an image directly influences the outcomes of classification traditionally based on NLP techniques. This paper introduces a novel multimodal approach that integrates textual and visual information to enhance accuracy, particularly in challenging OCR scenarios. We showcase practical applications highlighting the potential of our approach to improve document classification. The results to be presented in this paper seem interesting and appealing, demonstrating an improvement of accuracy.