- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
A
C
D
G
L
M
N
P
R
S
T
X
- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
Document AI
What is Document AI?
Document artificial intelligence (AI) is a technology that uses machine learning (ML) and natural language processing (NLP) to read and understand printed or written text, mimicking a human. It can be trained to analyze an organization’s documents to find relevant information and data insights, and can categorize and label the documents to make them easier to utilize. Many businesses have years of data stored in documents such as financial statements, receipts, contracts, invoices, etc. Document AI technology helps organizations analyze and digitize these documents much faster than they could by hand, while reducing human error.
How Does Document AI Work?
Document AI goes through five steps in order to learn how to effectively analyze an organization’s specific documents:
- Ingest - The AI is provided with a wide variety of documents. Document AI automatically recognizes various data types and formatting within the documents.
- Label - The initial data provided to the model must be labeled in order to prepare the data for the training process. Labeling existing data shows the AI model what to look for in the documents it analyzes.
- Train - The AI learns from the document set and creates an ML model specialized to the desired tasks.
- Deploy - The newly created model must be deployed into a workable environment so it can be used with new input documents.
- Consume - The model is ready to be used for business applications. Users continue to provide oversight and make any necessary adjustments.
Once trained, a Document AI model will be able to read and recognize text and other data formats, extract desired data from new documents based on prepared input, and generate important insights based on the data extracted.
Use Cases for Document AI
Document AI is especially useful for tasks where a large number of documents containing potential insights must be analyzed quickly and efficiently. There are several industries in which this is useful:
Legal Documents - Lawyers are often responsible for interpreting many documents related to their cases and clients. Whether they are reviewing laws and regulations, preparing for a case, or going over contracts, documents and information are their bread and butter. Document AI helps legal teams digitize and sort through documents to find the most relevant information.
Insurance Agencies - When insurance companies take on new commercial clients, reams of data and documentation must be analyzed to fully understand a client’s needs and risks. Document AI improves this process by automating administrative tasks and providing important insights from the data provided.
Banking/Finance - In commercial banking it is often necessary to review a large amount of documentation in order to understand financial and legal risk. Document AI can process and analyze financial documents to aid in client onboarding and loan approval.
Benefits of Document AI
In the information age, accurate and complete data are the keys to business success. However, it is estimated that in most companies, 80% of available data is in an unstructured format such as free-form text or hard copy documents. Traditional document processing requires human analysis of each document in a time consuming process. Document AI technology can analyze, organize, and learn from these documents quickly, saving time, money, and manpower to be used in other areas. This leads to more productive and efficient workforces and helps businesses to make well informed business decisions. Document AI also eliminates the opportunity for human error in document processing, creating accurate and consistent data. Document AI can also be used to label and organize unstructured data in order to prepare it for future use.
H2O Document AI
H2O.ai offers a premium Document AI solution to help organizations efficiently process their documents and extract information and insights. Some of H2O Document AI’s features include:
Comprehensive Document Processing - Equipped with natural language processing and optical character recognition, H2O Document AI is able to process and extract data from a variety of document formats and transform unstructured text into usable data to drive decision making and predictions.
Automated Data Labeling - H2O Document AI automatically creates labels for unlabeled documents, fixes labeling errors, and provides an intuitive user interface that simplifies the annotation process. It integrates with common label formats and provides advanced options for label validation.
Intelligent Information Extraction - H2O Document AI is capable of generating highly accurate results in a short time frame. By combining a multitude of machine learning and character recognition algorithms It is able to quickly glean the most important information from company documents.
Seamless Process Integration - H2O Document AI integrates easily with existing applications and workflows via the REST API. Models can be monitored and managed easily through H2O MLOps which operates within the H2O AI Cloud.