While IDP systems have made significant advancements in automating document processing tasks, there are certain complexities that make classification difficult. Here are some reasons why classifying documents remains a challenge in IDP:

  • Document Variability: Documents come in various formats, structures, and languages. They can range from simple text-based files to complex documents with images, tables, or mixed media. Each document type may have its own unique characteristics and layout, making it challenging to develop a one-size-fits-all classification model that works accurately across all document types.
  • Unstructured Data: Many documents contain unstructured data, such as free-form text, which lacks a predefined format or consistent organization. Extracting relevant information from unstructured data requires advanced natural language processing (NLP) techniques, including text analysis, entity recognition, and semantic understanding. Developing robust models to classify unstructured data accurately is a complex task.
  • Limited Training Data: Building an accurate document classification model typically requires a substantial amount of training data that represents various document types. However, obtaining labeled training data can be time-consuming and costly. Additionally, the availability of labeled data for specific document types or domains may be limited, leading to difficulties in training models with sufficient accuracy.
  • Evolving Document Types: New document types and formats are constantly emerging, especially with the increasing use of digital documents and evolving business practices. Existing classification models may struggle to accurately categorize new document types that were not encountered during the training phase. Adapting and updating the models to handle evolving document types in real-time can be a challenge.
  • Subjectivity and Domain-Specific Knowledge: Document classification often requires domain-specific knowledge or expertise to accurately categorize documents based on their content. Some documents may contain subjective or domain-specific terms, making it difficult to develop generic models that work well across different industries or specialized domains. Incorporating domain-specific knowledge into the classification models can be challenging.