The DocDigitizer Classifier is a rapid document classification tool that classifies with meta data and groups similar documents by folders.

The DocDigitizer Classifier is a rapid document classification tool that accurately identifies the type of document, such as contracts, invoices, or ID documents, without requiring human intervention. Its primary aim is to streamline digital transformation initiatives.

We developed this classifier as a solution for situations where businesses and IT departments require document type information, but a full Intelligent Document Processing (IDP) deployment is not necessary or suitable.

The DocDigitizer Classifier can determine the document type and extract associated metadata without the need for a comprehensive IDP solution. It offers two versions:

  • Standard, which can be installed on either the cloud or on-premises with a connection to the cloud for the OCR engine
  • Vault, which is installed solely on-premises and runs the OCR engine locally, ensuring zero connection to the internet for enhanced security of highly sensitive data.

Designed to analyze recurring, real-time samples of incoming documents and their attached metadata, the DocDigitizer Classifier is particularly well-suited for large companies and enterprises that handle substantial document volumes. It utilizes self-learning capabilities and is ideal for scenarios where rapid processing is required without the need for the very high accuracy (over 99.99%).

Typical use cases for the DocDigitizer Classifier include

  • Ingesting documents from multiple sources and performing initial categorization
  • Prioritizing documents for processing
  • Handling historic document databases.
  • Digital customer interactions where customers upload or send documents
  • Reorganizing internal document databases
  • Classifying and indexing internal document repositories.

The DocDigitizer Classifier is agnostic of document storage and source, and DocDigitizer provides drivers to connect with common sources such as the cloud, filesystem, Office 365, and Google Workspace. With its small infrastructure footprint, it is an ideal choice for business and IT teams looking to deploy tactical document processing solutions without incurring the high costs and complexities associated with full-scale IDP projects.

The pricing structure of the DocDigitizer Classifier ensures a low processing cost per document, making it a cost-effective choice for organizations seeking efficient document classification capabilities.


See our features

Classify documentsyesyes
Metadata augmentationyesyes
Import customer ontologiesyesyes
High speed throughputyesyes
Virtualization Supportyesyes
DeploymentCloud / OnPremOnPrem
OCR EngineCloud / OnPremOnPrem
Use internal licensed customer OCRyesEvaluate
Support for HSM (Hardware Security Module)yesYes.HSM to be supplied by customer
Internet download of classification databaseyesyes

How it works?

DocDigitizer document classifier provides a simple yet effective solution for customers who are specifically looking for document classification capabilities without the need to deploy a full Intelligent Document Processing (IDP) product. This module caters to customers who may have cost or complexity constraints associated with implementing a comprehensive IDP solution. Here's why it offers a straightforward alternative:

  • Streamlined Document Classification: The module focuses specifically on document classification, delivering a streamlined solution that excels in accurately categorizing documents based on their content, layout, and text patterns. By narrowing down its scope to classification alone, the module offers a simplified approach that addresses the specific needs of customers who prioritize this aspect of document processing.
  • Cost-Effective Solution: For customers concerned about the cost associated with deploying a full-fledged IDP product, the standalone document classifier module offers a more budget-friendly alternative. By targeting a specific functionality, the module optimizes cost efficiency, allowing organizations to benefit from document classification capabilities without investing in additional features that may not be essential to their requirements.
  • Reduced Complexity: Deploying and managing a comprehensive IDP system can be complex, requiring significant resources and expertise. However, the standalone module streamlines the implementation process by focusing solely on document classification. This simplicity translates into easier deployment, integration, and maintenance, making it accessible to customers who prefer a straightforward solution.
  • Ease of Integration: The standalone module is designed to integrate seamlessly with existing core systems, RPA platforms, BPM software, or other document processing workflows. It provides APIs and connectors that enable smooth integration, ensuring compatibility with the customer's existing infrastructure. This simplifies the adoption process, as the module can be easily incorporated into the existing technology stack without major disruptions
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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.