Glossary
Glossary
- Updated on Jun 7, 2026
- Published on May 21, 2025
- 30 minute(s) read
This article provides a list of terms as a reference to help you better understand key concepts related to the Hyperscience platform. It clarifies commonly used terminology, ensuring consistency across documentation and conversations. By using these definitions, you’ll gain a clearer understanding of how our platform works and how to make the most of its features.
A
| Term | Definition | Reference |
|---|---|---|
| Accuracy | Accuracy measures the effectiveness of the models based on the proportion of correct predictions out of all predictions made. It helps you understand how often the system correctly predicts values compared to the actual values that reached consensus during QA. Accuracy can be influenced by factors like imbalanced datasets or inconsistent annotations. | Accuracy |
| Additional Layouts | A layout type used to categorize pages where no data is extracted (e.g., a fax cover sheet). These layouts allow users to define custom categories for unmatched pages, helping to improve classification accuracy. Additional layouts can apply to Structured, Semi-structured, or Unstructured documents. | - Creating Additional Layouts - Understanding Document Types |
| Average Handling Time | A metric that represents the average time it takes to process a submission. The value is averaged across multiple documents or submissions within a specific date range. | Operational Value Reporting |
| Annotation | A user-provided input that defines the correct prediction for a given machine learning task. Annotations are used to train supervised machine learning models. | - Text Segmentation - Training a Semi-structured Model |
| Anomaly | A potential inconsistency or error in how a field or table is labeled in a document. Anomalies are flagged to help ensure consistent and accurate training data, which improves model performance. | Labeling Anomaly Detection |
| API Blocks | These blocks enable Hyperscience to interact with external systems through APIs, facilitating tasks like data retrieval, validation, or sending information to other applications. | Flow Blocks |
| Auto Thresholding | An automated process that calculates a confidence threshold based on a target accuracy. Predictions below this threshold are sent to Supervision for a human review to ensure the system meets the desired accuracy. | - Transcription Accuracy and Automation - Accuracy - Automation |
| Automation | The processing of data without the need for human intervention. | Automation |
| Automation Rate | The extent to which a machine can process data independently without requiring human supervision. It represents the proportion of extracted data with confidence scores exceeding a specified threshold. This threshold is determined by the level of accuracy you want your extracted data to have. | Automation |
| Auto-Splitting | A feature in Hyperscience that automatically groups pages into documents using rules you define. It helps organize Semi-structured documents by deciding where one document ends and another begins based on page count, text patterns (like titles), or layout-specific logic. | Auto-Splitting |
B
| Base Model | A foundational model that provides core general-purpose capabilities and is not directly trained on customer-specific examples. Use-case specialization is achieved through additional training on top of the base model using customer-specific data. | |
| Bounding Box | A rectangular subregion of a given page that specifies the location of text to be processed downstream or to be displayed to the user. | Segmentation |
| Bundle | A packaged file that contains everything needed to install or upgrade the Hyperscience platform. It includes the application and all required tools, helping to streamline setup and upgrade processes. | - Installing Hyperscience - Upgrading Hyperscience |
| Bypass Validation if Layout ID is Missing | A flow-level setting that bypasses validation by layout identifier if the matched Structured layout variation doesn’t have an identifier specified. The bypass allows the system to continue classifying documents even without layout identifiers, ensuring that documents that are not tied to a specific layout variation are still processed. | Structured Classification and Layout Identifiers |
C
| Calibration | A quality check performed after QA on Structured documents to evaluate model performance. It helps set target accuracy levels, define baseline automation thresholds, and assess how well different layouts, fields, or data types are processed before going live. | Contact your Hyperscience Representative for more information. |
| Case | A group of related documents, files, or pages that are processed together using a unique Case ID. | Case Collation |
| Cell | A value in a table that holds a single piece of data, such as a name, number, date, or multiline entries like an address or description. In Hyperscience, cells are key to reading and extracting data from tables accurately. | Table Identification |
| Character | Any single letter, number, or symbol found in a document. Hyperscience reads characters to understand and extract text. | - Supported Characters and Data Types - Default Data Types |
| Checkbox | A non-text field used to capture two-option answers like “Yes/No” or “True/False.” | Checkboxes and Signatures |
| Classification Model | A machine learning model that automatically identifies a document’s type—Structured, Semi-structured, or Additional—and matches it to the correct layout. This classification helps Hyperscience process different document types accurately without manual intervention. | - TDM for Classification Models - Understanding Document Types - Document Classification |
| Classify Using Layout Identifier | A flow-level setting that allows Structured documents to be matched using a layout identifier. See Layout Identifier for more context. | Structured Classification and Layout Identifiers |
| Clustering | The process of grouping similar documents or data points based on shared characteristics, often using machine learning algorithms. Clustering helps the platform to better organize and interpret large volumes of data by recognizing patterns and similarities. | Document Drift Management (Layout Triage) |
| Collation | The process of grouping related files, documents, or pages into a single case using a unique identifier called a Case ID. For example, if you submit multiple documents for a loan application, collation ensures that all these documents are grouped together under one case for streamlined processing and review. | Case Collation |
| Column | A list of values in a table that are of the same type of information, like names or prices, with one value per row. In simple tables, columns usually appear as vertical sections. However, in more complex tables, columns may not follow a vertical layout but still represent the same kind of data across rows. | Table Identification |
| Consensus | A process used to confirm the correct value of a transcribed field. Consensus is reached when two matching transcriptions are provided for the same field, usually one from a human and one from the machine or two separate human-provided entries. This process ensures higher accuracy, especially when the system's confidence is low. | Transcription Supervision Consensus |
| Continuous Field Locator model improvement | When this setting is enabled, the system automatically retrains and updates Field Locator models using newly available QA data. This process allows the model to improve over time without manual intervention. It helps enhance accuracy for identifying field locations in Semi-structured documents. This setting should only be enabled if there’s enough training data in the environment to support it. | Identification Settings |
| Copycat | After you’ve annotated a single row from a table, you can use the copycat feature to copy the annotations to the remaining rows of the table. The copycat is not always accurate, so make sure to double-check the annotations before you submit. | - Training a Semi-structured model - Table Identification |
| Crop | An image of the specific field you want to extract. | Text Segmentation |
| Custom Code Block | A flexible component in Hyperscience flows that allows you to add custom Python logic to transform, validate, or enrich data before it's sent to downstream systems. It lets you apply your own business rules as part of document processing. | - Flow Blocks - Modifying Custom Code Blocks |
| Custom Data Type | A user-defined format that tells Hyperscience what a specific type of data should look like, such as a Social Security Number or a policy ID. Custom data types help the system validate and extract field values more accurately based on expected patterns. | - Creating Data Types with ML Configurations - Creating Data Types with a List of Expected Values - Creating Data Types with Custom Patterns |
| Custom Supervision | A configurable task in Hyperscience that you can tailor to your business needs. It allows you to manually review, validate, or enrich data using flexible logic, custom fields, and decision types. | Custom Supervision |
D
| Database Block | A specific type of block that allows you to connect Hyperscience to external databases. These blocks allow the system to fetch or validate information during document processing. | Flow Blocks |
| Data Extraction | The process of pulling specific information—like names, dates, or amounts—from a document. In Hyperscience, extraction happens after a page is matched to a layout and uses trained models to identify and capture the right data. | |
| Dataset | A group of documents used to help the system learn or improve. Datasets are used for training, testing, or evaluation of how well the system reads and extracts information. | |
| Data Type | A property that defines the format of the data expected in a field, like numbers, dates, or email addresses. For example, the data type Date accepts only valid dates (e.g., MM/DD/YYYY). Data types help Hyperscience understand what’s expected in a field and flag anything that doesn’t match. | What is a Data Type? |
| Deployed Model | A trained machine learning model that has been activated within Hyperscience to process documents in real time. Once deployed, the model is live and is used to process documents—classifying them, locating fields, and extracting data based on what it has learned. | Training a Semi-structured Model |
| Document | A group of one or more pages processed as a single unit in Hyperscience. Documents are categorized as Structured, Semi-structured, or Additional based on how consistent their layouts are and how fields can be extracted from their pages. | Understanding Document Types |
| Document Classification Quality Assurance | A task where you review and confirm whether Hyperscience correctly identified the type of each page. This process helps improve the system’s ability to match pages to the right layout, supports the training of the Classification model, and is used for document-classification reporting. | Document Classification |
| Document Classification Task | The first step in Supervision. It is used to categorize and combine pages that were not classified by the machine. | Document Classification |
| Document Drift Management (Layout Triage) | A post-processing feature that helps you manage documents that don't match layouts during Classification. When submissions don't meet the Structured Layout Match Threshold or are manually flagged as having incorrect or missing layouts, their pages are marked as unmatched. | Document Drift Management (Layout Triage) |
| Document Eligibility Filtering | A feature in Training Data Management that indicates whether a document is eligible for training based on internal checks in the application and our machine learning logic. It provides additional information about documents that were excluded from the training set. | Document Eligibility Filtering |
| Document Renderer Block | A step in a Hyperscience flow that turns processed documents into downloadable PDFs and generates links to access them. You can customize the page size and image quality of the PDFs to meet your needs. | Flow Blocks |
| Dropout | A field-level setting that tells the system to ignore background text like pre-printed labels or symbols. When enabled, the system removes this background content and transcribes only new or handwritten text, helping to improve accuracy. This setting is enabled by default. | Training a Structured Model |
E
| Eligible Number of Documents | The number of documents that meet the requirements for training Identification models in Hyperscience. A minimum of 100 is needed to train, with 400 recommended for best results. This number applies to Field Identification and Table Identification models. | - Training a Semi-structured model - Document Eligibility Filtering |
| “.env” File | A configuration file used to define environment-specific variables, such as API keys or database credentials. It allows Hyperscience to run securely and consistently across different instances. | |
| Excluded Documents | Documents added to a Classification training set to show the system what should not be matched to a specific layout. They help improve model accuracy by teaching your model to ignore documents that look similar but don’t belong. | TDM for Classification Models |
F
| False Negatives | A type of error or outcome in machine learning evaluation. In Hyperscience, a false negative happens when the model fails to extract a field value that is clearly present and should have been captured. False negatives apply to Field Identification and Table Identification models. | |
| False Positive | A type of error or outcome in machine learning evaluation. In Hyperscience, a false positive happens when the model predicts a field value in the wrong location or extracts something that shouldn’t be considered a valid field at all. False positives apply to Field Identification and Table Identification models. | |
| Field | A labeled piece of information you want to capture from a document, like “Name,” “Date of Birth,” or “Total Amount.” In Hyperscience, fields allow you to specify the values that will be extracted from your documents. | Field Identification |
| Field Customization | A feature that allows you override a field's default settings on a per-release basis. For example, you can set a layout's |