A Complete Guide to Document Classification

Automated document classification is a revolutionary technology that uses advanced algorithms and machine-learning techniques to categorize documents based on their content. It uses algorithms that use NLP and AutoML.

If the data is small, it can be based on Neural Networks (Deep Learning), Naive Bayes classifiers, or Logistic Regression. This list is inexhaustive.

In intelligent document processing, both supervised and unsupervised ML techniques classify documents automatically. The supervised model is popular for its accuracy and uses a trained dataset. Depending on the algorithm, the model may provide a confidence score and other metrics to show its classification accuracy.

What is document classification?

Document classification, or document categorization, is classifying documents or classes. Automated document classification is becoming popular among many companies. It enhances storage, analysis, document management, and operations. Using this technology instead of manually classifying vast amounts of data eliminates inefficiencies, saves time, and lowers mistake rates.

Methods of document classification

Manual classification

Although it is feasible for small-scale operations but not large businesses, manual classification requires human effort to categorize documents. The following are some limitations of manual classification:

• Time consumption: Manually classifying documents takes a lot of energy and time.
• Subjectivity: Human judgment is prone to biases and errors that impair classification accuracy.

Automated classification

Automated document classification uses machine learning and algorithms to effortlessly and accurately classify documents. This process consists of multiple steps:

• Determining the file format involves identifying if the document is a jpeg, png, or pdf.
• Identifying document structure: Classifying documents into three categories: semi-structured, unstructured, and structured.
• Identifying the document type involves using pre-processing strategies, tagged datasets, and classification processes.

Classification Techniques

Visual approach

It analyzes the document’s visual structure using computer vision without reading the text. It works well for structured and semi-structured documents with consistent patterns and layouts.

Text classification approach

Optical Character Recognition (OCR) in text classification analyzes the text and classifies documents according to the information it extracts. This approach is capable of multiple levels of text analysis:

• Document level: Reading a document’s entire text.
• Paragraph level: Analyzing the text in a specific paragraph.
• Sentence level: Analyzing individual sentences.
• Subsentence level: Concentrating on specific phrases.

Automated document classification techniques

• Computer Vision features recognition: This method categorizes documents according to their visual structure by dissecting them into small pixels and looking for patterns.
• Textual Recognition: This procedure uses Natural Language Processing (NLP), rule-based text recognition, and optical character recognition (OCR) to identify text with context.

Textual recognition document classification methods

Optical character recognition (OCR)

OCR reads characters from scanned documents and transforms them into digital text. This technology makes data entry and classification easier, error-free, and efficient.

Rule-based text recognition

This method recognizes and classifies semantically meaningful elements in a text based on predefined rules. Although efficient, it requires extensive domain expertise and time to maintain.

NLP-based document classification

Lexical and semantic content analysis is how NLP systems classify documents. Tokenization, word stemming, stopword removal, and other techniques help classify documents according to their text content.

Implementing document classification with Python

Automated document classification with Python can be accomplished by:

• Importing libraries: Among the necessary libraries are sklearn, pandas, numpy, and spacy.
• Data preprocessing: Utilizing regex, stemming, and stopword removal to clean and preprocess text data.
• Feature extraction: Using techniques to translate text into numerical features, such as TF-IDF.
• Model training: Machine learning models — like Naive Bayes classifiers — are trained to classify texts based on extracted features.
• Evaluation: Evaluating the model’s F1 score, accuracy, precision, and recall.

How Docsumo stands out for document classification

Docsumo’s user-friendly interface makes document classification simple. To begin with, go to “API and Services” and enable the document types you want to classify. After ensuring that every chosen document type has been trained against a minimum of 20 documents, activate “Auto-classification.”

Then, upload all of the documents at once into the auto-classification section. The platform will classify documents based on their kinds in an intelligent way.

You can also automatically assign team members various document types for review and approval. In addition, Docsumo offers several advantages over hardcoding techniques, such as cost savings, fewer errors, and more advanced semantic analysis.

It ensures data security with GDPR compliance, SOC-2 certification, and strong encryption. With plug-in APIs and interfaces, Docsumo easily integrates into existing workflows.

Are you curious about how Docsumo can make document processing more efficient? Register now for a 14-day free trial! We would be delighted to discuss your business use case and learn how we can support you!

Wrapping up

Automated document classification effectively lowers errors, saves time, and improves document management efficiency. Businesses can simplify document processing with advanced ML and NLP tools. It frees up time for more essential activities.

Manual classification is still common, but automation is key for improving accuracy and productivity.