ORCA (Optical Reasoning and Cognition Agent) is Hyperscience’s Vision Language Models (VLM) framework for extracting information from different documents.
Unlike traditional extraction models, ORCA combines language understanding with visual reasoning, allowing it to interpret document structure and extract information from complex layouts.
Accessing this feature
Your access to the feature described in this article depends on your license package and pricing plan.
To learn which features are available to your organization and how to add more, contact your Hyperscience representative.
ORCA works out-of-the-box as a pre-trained base model. However, you can also train specialized models on your own documents to improve extraction accuracy for your specific use case. In this article, you'll learn:
what ORCA is
which ORCA base models are available
how ORCA models work together
where to go next to install, configure, and train specialized models.
Key benefits
ORCA is designed for organizations that need to process different types of documents, such as structured, semi-structured, or unstructured.
Using ORCA allows you to:
Reduce implementation time by using a pre-trained base model.
Process a wide variety of document types.
Improve field extraction accuracy by training specialized models on your own documents.
Extract information using both textual and visual context.
Extract tables with ORCA 2 without a layout.
VLM field and table extraction are separate capabilites
VLM Field Extraction model definitions and specialized-model training are field-based. Their associated Semi-structured layout must contain at least one field.
ORCA 2 table extraction uses a separate Table Extraction Flow and does not require a layout.
ORCA VLMs architecture
ORCA uses a layered model architecture:
A base model provides general-purpose document understanding and extraction.
A specialized model is trained on top of a specific base model, using annotated examples specific to your use case.
A Model Definition groups the base and specialized models associated with one semi-structured layout.
Specialized models inherit the capabilities of the base model
A specialized model is always tied to the base model it was trained on: it cannot be transferred between base models, and a model trained on ORCA 1.0 cannot be used with ORCA 2, or the reverse.
ORCA base models
Starting in v43.2, Hyperscience supports multiple ORCA base models. The base models available in your instance depend on your license.
Base model | Available in |
|---|---|
ORCA 1.0 | v42.0.2 and later |
ORCA 2 | v43.2 and later |
ORCA 1.0
ORCA 1.0, introduced in v42.0.2, enables GPU-powered Vision Language Model extraction in the Hyperscience platform. It provides general-purpose field extraction capabilities and serves as the foundation for use-case-specific specialized models. ORCA 1.0:
Extracts fields from Semi-structured or unstructured documents.
Supports specialized models.
Requires a Semi-structured Layout with at least one field.
Does not support out-of-the-box table extraction.
ORCA 2
ORCA 2 is the latest generation of Hyperscience’s VLM framework. ORCA 2 introduces out-of-the-box table extraction, along with improved accuracy and automation. Unlike ORCA 1.0, ORCA 2 can extract tables without requiring a predefined Semi-structured Layout or a trained Table Identification model.
Extracted table data is returned in table format, with the rows and columns detected in the document.
ORCA 2 does not replace Table Identification for every use case
Table Identification remains available when layout-specific extraction is required. Learn more in Identification Models Overview.
Choosing a base model
The capabilities available depend on the ORCA base model you're using:
Capability | ORCA 1.0 | ORCA 2 |
|---|---|---|
Field extraction | Yes | Yes |
Specialized models | Yes | Yes |
Out-of-the-box table extraction | No | Yes |
General prompting | Yes | Yes |
We recommend using ORCA 2
ORCA 2 is the recommended base model for new implementations and future specialized models.
If you already use ORCA 1.0, your existing specialized models continue to work with ORCA 1.0. Existing ORCA 1.0 deployments continue to be supported.
Learn more about ORCA’s general prompting capabilities in General Prompting Block.
Migrate from ORCA 1.0 to ORCA 2
ORCA 2 is recommended for new deployments because it provides improved document understanding and out-of-the-box table extraction. Learn how to migrate in Training a Specialized Model.
Specialized models cannot be transferred between ORCA 1.0 and ORCA 2
Each ORCA base model requires its own specialized model.
Get started with ORCA VLMs
Before processing documents with ORCA, make sure your instance is configured correctly.
Infrastructure requirements
ORCA requires a Graphics Processing Unit (GPU) and training a specialized model requires a Trainer
ORCA requires a GPU-enabled application machine for inference. See Infrastructure Requirements for more information.
Training a specialized model requires a trainer machine with a GPU. See Kubernetes Installation Overview.
ORCA requires a minimum of 24 GB of VRAM for inference, and a minimum of 24 GB (48 GB recommended) for training. GPUs below these amounts are not supported.
For deployment-specific instructions:
On-premise deployments — follow the steps outlined in “Enabling Application Machines with GPUs” in the following articles:
SaaS deployments — contact your Hyperscience representative for more information.
Install an ORCA base model
Before using ORCA, install the required base model on your instance.
For installation instructions, see Installing ORCA VLMs.
Processing requirements
Workflows
To process submissions with ORCA VLMs, your implementation must use one of the supported processing flows. Depending on your use case, you can use:
Document Processing with ORCA subflow
VLM QA subflow
a custom flow configured to use the ORCA blocks required for your implementation.
For information about configuring ORCA flows, see Document Processing with ORCA Subflow Settings and Flow Blocks.
VLM Quality Assurance (QA)
To generate accuracy reporting for ORCA VLM extraction, you need:
the VLM QA Subflow selected in the Document Processing with ORCA subflow
QA sample rate.
The QA subflow is not selected by default
If QA is not configured, no QA tasks are created, and accuracy reporting is not available. Learn more in Installing ORCA VLMs.
Control model selection in a flow
In v43.2, the ORCA Extraction Block includes a Use live model setting. It determines how Hyperscience selects the model used for extraction.
When Use live model is selected, Hyperscience uses the live specialized model associated with the layout.
Processing stops if no live model is available.
When Use live model is not selected, the base model configured in the flow controls processing.
Hyperscience uses a specialized model only if it was trained with that base model. Otherwise, it processes the document with the base model alone.
Configure Use live model in the Flows Studio > Edit Flow > ORCA Setting type.
Learn more in Document Processing with ORCA Subflow Settings.
Flexible Extraction
To review low-confidence predictions with Flexible Extraction, you need:
Flexible Extraction configured in the Document Processing with ORCA Subflow. Learn more in Installing ORCA VLMs.
Semi-structured Layout
To process documents with ORCA VLMs, you need:
a Semi-structured layout with at least one field.
If your implementation processes both Structured and Semi-structured documents, configure a custom flow that routes each document type to the appropriate processing path. For more information, contact your Hyperscience representative.
ORCA 2’s out-of-the-box table extraction does not require a semi-structured layout.
A Release assigned to the Document Processing with ORCA subflow or any custom flows with ORCA blocks.
General-purpose extraction
After you install and configure ORCA, you can start processing documents.
Because ORCA is delivered as a base model, it provides general-purpose extraction capabilities and is not adapted to your specific document types or business requirements. To adapt the base model to your use case, you must train a specialized model.
Training a specialized model
To specialize a base model for your specific business case, you need:
A model definition.
annotated Training Data that represents your use case.
For more information, see Model Definitions, TDM for ORCA VLMs, and Training a Specialized Model.