DOCUMENT INTELLIGENCE
Read Beyond the Text. Reveal the Information Inside.
AIKNOW Capture interprets tables, diagrams, formulas and the context across pages—not just individual characters. It brings document layout and content together, turning technical information into structured data for search, analysis and AI applications.
Design materials, technical reports, papers and specifications hold important information beyond their text.
Key conclusions and conditions are sometimes expressed not in sentences but in the structure of a table, the trend of a graph, a formula or the relationships within a figure.
Plain-text extraction can omit the relationships needed to interpret these elements.
OCR recognizes text in scanned pages and images.
Technical documents may also require layout analysis, table extraction and interpretation of figures. The processing and checks needed depend on the document and the intended use.
Converting PDFs and images to plain text can discard document structure and relationships between elements.
Missing structure can make relevant information harder to retrieve and interpret in retrieval-augmented generation (RAG) applications.
Even when documents are stored and searchable, engineers may need to inspect several files to find the relevant technical context. Structured extraction can complement existing document management and search systems.
AIKNOW Capture analyzes page layout and relationships between document elements.
It uses document structure and cross-page context to organize extracted information for downstream use.
Recognizes headings, multiple columns and annotations in reports and academic documents.
Identifies reading order and information structure to organize extracted text.
Links related content that continues across columns or pages.
Uses document-level context to interpret those connections.
Identifies chapters, sections, lists, quotations and footnotes, and organizes the data while preserving their hierarchy.
Extracts table content and structure, including merged cells and checkbox states.
Extracts formulas from mathematics, physics, chemistry and other fields, and can also output them in LaTeX.
Analyzes figures, graphs, images and design drawings alongside the surrounding text.
Uses image correction and OCR to process text in faded or skewed images. Recoverable detail depends on the source quality.
Supports PDF, Word and Excel documents.
Processing requirements depend on the file content, layout and quality, including whether the material is scanned or digitally generated.
The comparison below contrasts a text-only extraction workflow with AIKNOW Capture’s multimodal processing. OCR is also part of AIKNOW Capture’s pipeline.
This workflow produces recognized text.
If the output is limited to plain text, it may omit table structure, graph trends, links between figures and text, and relationships across pages.
Additional processing may be needed to recover these relationships for the intended application.
Analyzes text, tables and figures together and structures the extracted content and relationships for downstream use.
Processes images and text together to interpret varied layouts without requiring a fixed template for every document. Application-specific configuration and validation may still be needed.
Image correction and layout analysis prepare inputs for recognition. Post-processing uses matching, correction and confidence scoring to help identify inconsistencies, including in numbers and proper nouns.
Five stages combine preprocessing, multimodal analysis, semantic interpretation, validation and structured output. Extraction quality depends on source quality, document complexity and processing configuration. Extracted results are automatically made available for downstream use without a mandatory review of each result. Quality requirements and any additional checks for the intended application should be assessed using representative documents during a POC.
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Information expressed in forms other than text is analyzed together, as part of the document.
Analyzes documents in different formats without fixed templates.
The recognition pipeline is designed for Japanese documents, combining OCR, a VLM, and pre- and post-processing.
It converts Japanese business and technical materials into a form that can be used as knowledge.
Includes image correction, alignment, matching and confidence scoring. Confidence scoring varies by document type and needs validation against representative documents during a proof of concept (POC); it should not be read as a guaranteed probability of correctness.
Recognition runs on GPUs, while preprocessing, matching and confidence calculation run in parallel on CPUs.
Throughput depends on hardware, concurrency and document complexity.
Produces structured data that represents document elements and their relationships alongside extracted text.
The output supports integration with RAG applications, knowledge graphs and generative AI platforms. Output requirements and mappings depend on the target system.
In an internal test using a two-page handwritten form, the AIKNOW Capture configuration recorded approximately 30 times the page throughput per server of the reference configuration. The CPU and RAM configurations were the same; AIKNOW Capture additionally used an NVIDIA L40 GPU.
30× processing throughput (approx.)
Observed page throughput per server in this test.
Test scope: 20 runs using the same sample, a two-page form completed by hand. Both configurations used the same CPU and RAM configuration; the AIKNOW Capture configuration additionally used an NVIDIA L40 GPU. Reported throughput was 0.051 and 1.539 pages per second per server, respectively. The reference processing method, exact CPU/RAM specifications, concurrency, measurement date, aggregation method and output-quality criteria are not specified here. These figures describe this internal test, not a general performance or cost guarantee. They do not establish equivalent extraction quality or isolate the contribution of the processing software from the added GPU. Representative documents and deployment conditions should be evaluated in a POC.
Analyzes past design materials, specifications and test reports to build a knowledge base where engineers can find and reuse the technical information they need.
Analyzes academic and technical documents that contain many figures, tables and formulas.
Research findings and technical information can then be used across sources.
Structures product manuals and specifications with complex chapter structures, tables and figures.
Converts them into knowledge that RAG and AI assistants can use.
Extracts and structures content from paper scans and legacy documents for downstream search, analysis and reuse. Poor image quality or missing detail can limit extraction.
Discuss your document types, output requirements and target applications.
We can assess how AIKNOW Capture can prepare structured technical content for retrieval and reuse.
