--- title: "Structured Document Classification" slug: "structured-document-classification" updated: 2026-07-22T09:44:36Z published: 2026-07-22T09:44:36Z canonical: "help.hyperscience.ai/structured-document-classification" --- > ## Documentation Index > Fetch the complete documentation index at: https://help.hyperscience.ai/llms.txt > Use this file to discover all available pages before exploring further. # Structured Document Classification ## Classification models in Hyperscience Classification models are a crucial part of document processing as they help the system determine which layout should be used to process each page you upload. In Hyperscience, we have two types of document classification: - **Structured Document Classification** - Automatically classifies documents that follow a consistent layout pattern (e.g., tax forms, standardized applications) by assigning them to the correct layout in Hyperscience. Once classified, the documents can proceed to the next step - **Field Identification Task**, **Transcription Task,** or **Flexible Extraction,** depending on your business logic. - **Semi-structured Document Classification -** Automatically classifies documents that don’t follow a consistent layout pattern (e.g., invoices, bank statements, etc.). To learn more, see [Semi-structured Document Classification](/v43/docs/clone-v42-draft-semi-structured-document-classification) and [TDM for Classification Models](/v43/docs/tdm-for-classification-models). In this article, you’ll learn how to work with Structured Document Classification. Learn more about the Manual Classification task in [Document Classification Task](/v43/docs/document-classification-task). ## Structured Document Classification ### How it works When a document is submitted to the system, it is first split into individual pages. Structured Document Classification then runs through the following steps: 1. **Visual Page Classifier (Visual Page Classifier (VPC)):** - The system runs VPC, which returns layout page-level candidates for each submission page. At this stage, only candidates are proposed, not full documents. - For every page, the VPC produces a list of possible layout matches, ranked by confidence. To learn more about Structured layouts, see [Creating Structured Layouts](/v43/docs/creating-structured-layouts). - **Example**: Submission page 1 may match layout page 1 of Layout A; submission page 2 may match layout page 2 of Layout A, etc. 2. **Distribute to Forms:** - Using the list of candidates generated from the VPC, the system attempts to group pages into complete documents. - The goal is to minimize the number of documents while maximizing confidence scores across all pages. - Pages that fail to align with any layout at this stage are passed to Semi-structured classification (Non-Structured Layout Classifier (NLC)). To learn more, see [Semi-structured Document Classification](/v43/docs/clone-v42-draft-semi-structured-document-classification). 3. **Registration:** - For each proposed distribution, the system runs a Registration step to validate the page-to-layout matches. - If confidence for all candidates is above the acceptance threshold (e.g., >0.6), the document is accepted. - If one or more candidates are rejected, the system re-runs Distribute to Forms with alternative matches. 4. **Re-distribution & Finalization:** - The Distribution - Registration cycle runs up to three times: - **First attempt:** Initial grouping of candidates. - **Second attempt**: Re-distribution if a candidate is rejected. - **Third attempt:** Final re-distribution. - If all candidates are successfully registered, the current distribution is finalized and used. - If some candidates are rejected, the system generates a new distribution and retries registration. - If none of the attempts result in a fully valid distribution, the system returns the best-scoring distribution from the three tries. - This process repeats up to 3 times. 5. **Manual Review:** - Any pages that fail to classify after these steps are marked as **No Layout Found** and routed to Semi-structured Classification. Depending on your flows settings and your use case, these can be handled via Document Classification Supervision Task.To learn more, see [Semi-structured Document Classification](/v43/docs/clone-v42-draft-semi-structured-document-classification). > [!NOTE] > Blank Pages > > If a page contains very little text, VPC may match it as blank and will not attempt to classify it to a layout. #### Structured Layout Match Threshold The Structured Layout Match Threshold defines the minimum confidence score required for a page to be automatically matched to a Structured layout. - The default threshold is 0.6, but it can be adjusted to fit your use case. - Lowering the threshold may increase the risk of incorrect matches. > [!NOTE] > Layout Matching Confidence > > Confidence in layout matching directly affects the accuracy. The more confident the system is in its layout match, the more reliable the extracted data will be. To learn more, see our [Accuracy](/v43/docs/accuracy) article. > > Contact your Hyperscience representative to determine the best threshold for your use case. Expand the sections below to learn more about the Structured document classification settings and layout identifiers. ### Structured Document Classification Settings Before you start, configure **Structured** document classification behavior in your flow. - Enable **Manual Classification Supervision** to use the **Document Classification** task, as described in [Document Processing Subflow Settings](/v43/docs/document-processing-subflow-settings). - **Structured Layout Match Threshold** - This threshold controls whether a **Structured** page is matched to a layout. - Pages with confidence **below** the threshold are sent to **Document Classification** or marked as **No Layout Variation Found**. - Pages with a confidence score **above** the threshold are automatically assigned to a layout. - **Validate Classification Using Layout ID** - Enabling this setting allows Structured documents to be matched using a layout identifier. When this setting is enabled, the system checks for a matching layout identifier in the document. - **Bypass Validation if Layout ID is Missing** - This setting should be enabled when certain layouts do not contain a layout identifier. Learn more about these settings in the Classification section of our [Document Processing Subflow Settings](/v43/docs/document-processing-subflow-settings) article. ### Layout Identifiers #### Classifying Variations Some layouts can look almost identical, with only minor visual differences. To avoid misclassification in these cases, you can create layout variations. Each variation represents a small difference in the layout’s pattern, while still belonging to the same overall layout group. Learn more in [Adding a Variation to a Layout](/v43/docs/adding-a-variation-to-a-layout). #### Layout Identifiers Even with variations, the system may sometimes classify incorrectly. To improve accuracy, Hyperscience can use Layout identifiers to force the correct match. - If the identifier in the document matches the expected ID in a layout variation, the system will classify the document to that variation, regardless of the confidence score. - If the identifier does not match, the document is routed either to **Document Classification Task** or to **Document Drift Management (Layout Triage)**, depending on your flow settings: - With **Manual Classification** off, the document goes directly to **Document Drift Management**. To learn more, see [Document Drift Management (Layout Triage)](/v43/docs/document-drift-management-layout-triage) - With **Manual Classification** on, the document is reviewed further by a keyer. Learn how to work with Manual Classification in [Document Classification Task](/v43/docs/document-classification-task). #### Using Layout Identifiers 1. Go to **Library** > **Layouts**. 2. Find the layout to which you want to add a layout ID, and click on its name. 3. Find the variation to which you want to add a Layout ID, and click on its name. 4. Click **Fields** in the toolbar, and then click **Layout IDs**. ![](https://cdn.us.document360.io/87894cef-4958-4f3f-be6f-b75a78c82548/Images/Documentation/identifiers.jpg) 5. Click and drag to draw bounding boxes around each layout ID. - Once you draw a box, the machine will read and transcribe the value inside it. - Any incorrect transcriptions can be edited in the field list. 6. When you’re finished making changes to the variation, do one of the following: - If you’re ready to apply your changes to the variation, click **Commit Changes** and save it as a new version. Learn more in [Editing and Finalizing a Layout Version](/v43/docs/editing-and-finalizing-a-layout-version). - If you’re not ready to apply your changes, click the **X** button in the upper-right corner of the page. #### Layout Identifiers Best Practices The following best practices for using layout identifiers will ensure the highest levels of accuracy for the classification of Structured documents. - Use unique text at the top or the bottom of the page as a layout identifier. - The ideal layout identifier is a piece of unique text placed at the top or bottom of a page. Many documents include layout-specific versions, dates, or other identifiers in these areas. Using such identifiers helps reduce errors and improve the accuracy of matching pages to Structured layouts. - If no unique text appears at the top or bottom of the page, use another piece of variation-specific text elsewhere. - If no identifier is available at the top or bottom of the page, look for unique text within the document that appears only in a specific variation. For example, a clause included in just one variation can serve as a distinguishing factor. Using this text as a layout identifier helps the system correctly classify that variation. - Add only the layout identifiers required to tell similar layouts apart. - Each layout page should have only one identifier. In rare cases where multiple layouts share the same identifier, you can add a second one to distinguish between them. Avoid adding extra identifiers, as they reduce matching accuracy. - Structured layouts are limited to two identifiers. If you try to add more, the system displays a warning message. - Keep the number of layout identifiers consistent across all variations of the same layout. - If a layout has multiple variations, use the same number of identifiers across all of them. Otherwise, variations with more identifiers will receive a higher confidence boost, which may cause the wrong layout to be matched. ## A manual task in Hyperscience where you confirm or correct the location of fields in Semi-structured documents. You can adjust or draw bounding boxes around field values to help your model learn where to look for the data you want to extract. You may need to perform a Field ID task when the machine is not confident enough in its prediction for a field, based on the target accuracy. A Supervision task that allows you to review or enter text the system couldn’t confidently read from a document. This task enables you to ensure accurate final data when the system’s confidence is low. A task in Hyperscience that involves human intervention to validate or correct data extraction for Structured documents. This task is used when automatic extraction isn’t fully reliable, allowing you to transcribe or adjust specific fields to ensure accuracy. An automated component that matches submission pages to the correct layouts from the Layout Library. It ensures accurate and efficient processing of Structured documents. Finds the correct Semi-structured or Additional layout for a given set of submission pages based on the words in the submitted documents. Note that NLC works on a page level. The confidence limit used to decide if a machine prediction should be sent for human review to ensure accuracy. A string of characters that appears on a specific location on a page in a Structured layout. It allows the machine to distinguish among similar layouts that a submission matches to. It can contain letters, numbers, or a combination of both. Layout identifiers help the machine achieve the best match for submitted pages.