Classifying large volumes of documents is essential to make them more manageable and, ultimately, obtain valuable insights.
But manual processing might find the incoming volume of data very hard to manage, not to mention tedious and inefficient. That is why an automatic document classification is a great option. Using advance Natural Language Processing (NLP) techniques and machine learning algorithms, we can automatically classify and process n number of categories, which are faster and scalable.
Document processing is a field of research and a set of production processes aimed at making an analog document digital.
This includes extracting the structure/schema of the document or the layout and then the content, which can take the form of text or images. The process can involve traditional computer vision algorithms, convolutional neural networks, or manual labor.
Conventional Methods
In every industry like Health Care, Insurance, etc, in which paper plays a significant role, manual document processing causes problems.
Manual processing is slow and tedious. It requires employees to perform repetitive, monotonous tasks while adding very little value. It is also error-prone and not transparent, and thus often causes operational inefficiencies. Traditional document processing units required staff members to manually read and key in relevant information from documents — every day, year after year.
This process lowers both staff morale and productivity and often leads to unwanted errors and increased costs. On the other hand, document classification is the act of labeling — or tagging — documents using categories, depending on their content. Document classification can be manual (as it is in library science) or automated (within the field of computer science) and is used to easily sort and manage texts, images, or videos.
AI In Large Scale Document Processing
Depending on the complexity of document processing workflows, the cost savings driven by automation may not pay off compared to manual processing. Organizations that already employ sophisticated document processing systems have a competitive edge.
Using Natural Language Processing (NLP) and machine learning algorithms, like Bert Transformers, Random Forest, etc., we can automatically assign one or more categories to huge amounts of textual documents. Machine learning tools are faster, scalable, and reliable as compared to humans.
Types of ML Algorithms, we use during document processing:
Supervised: In this method, we will need to define a set of tags (let’s say, Insurance letters, Invoice) and manually tag a number of texts before machine learning models can start making predictions on their own.
Unsupervised: In this method, documents containing similar words or sentences will be grouped together by a classifier without any prior training. For example, the words Medicine, prescription, or hospital would be recognized as sharing similar qualities and grouped within the same cluster.
Rules-based: This method is based on linguistic rules that give instructions to models. Following these rules and patterns, which are based on morphology, lexis, syntax, semantics, and phonology, models will automatically tag our texts.
Benefits Of AI in Document Processing
- Reduce document processing cycle time: Cycle time is a key metric tracked by enterprises to measure effectiveness across all recurring processes. Reduced document processing cycle time opens new opportunities for higher profitability. It helps build resilient business processes and makes a company more competitive.
- Detect errors and anomalies before they reach end applications: AI and ML capabilities mean an IDP solution can automatically detect anomalies from the extracted data and notify the operator. And not only can the solution identify the factors that can affect your business operations immediately but extracted data also can be processed based on custom business rules before publishing it to the system of record.
- Process many documents coming from multiple channels: Document processing workflows too often suffer from a lack of streamlining, something that IDP addresses. An IDP solution, in contrast, can process multiple document types from various channels continuously and automatically capture documents from emails (aliases), FTP sites, and SharePoint. Documents in almost any format, be it PDF, XML, HTML forms, or something else, can be seamlessly processed and interpreted.
Algorithm Training
Dataset
This is the most important element we need to gather data for training the classifier. The dataset needs to contain enough documents or examples for each category so that the algorithm can learn how to differentiate between them. For example, if you want to classify documents into five categories, for training a classifier you would need at least 100–200 documents per category to achieve decent predictive capabilities.
Once we have the data to train the model, the next step is to use that data to train a classification algorithm. There are many complex algorithms that we are currently using including Machine Learning as well as Deep Learning. As per the data available, we will be using below multiple approaches.
Small Dataset: In this case, we are using SSD type approach, where we are identifying the real-time document with the labeled once with the help of calculating distance between their embeddings.
Medium-Sized Dataset: For this scenario, we have built multiple models which either we could use individually or in an ensembled manner. Few of the algorithms like Random Forest, Xgboost, Naïve Bayes, etc.
Large Dataset: For large Dataset, we have deep learning models which could learn the patterns more deeply and accordingly gives comparatively high accuracy. Apart from Deep Learning Neural Networks like Recurrent Models RNN, LSTM, etc. We are using Transformers like BERT, Roberta, etc. for a more contextual understanding of the document hence better accuracy.
Conclusion
Documents are some of the richest sources of information for any business. Be it articles, customer surveys, or support tickets, all of them contain valuable insights. The best way to get to these insights is by classifying all the data you receive so you can start making sense of them. Manual classification of documents can be a nightmare, especially if the volume of information is high. In this scenario, labeling documents becomes repetitive and human agents are likely to make mistakes. Document classification is much more efficient, cost-effective, and accurate when done by machines. Save yourself the hassle of manual analysis and start using machine learning for effective document classification. There are many classification tools available that make it super easy to start using AI for document classification; some of these tools don’t even need you to write a single line of code.
