Many teams struggle to turn raw PDFs, scanned images, or photos of documents into usable, structured data for downstream applications like search, analytics, or retrieval‑augmented generation. The core challenges include identifying where text, figures, and tables sit on a page, accurately reading the characters inside those regions, rebuilding the correct reading order, linking captions to their objects, and pulling out domain‑specific entities such as amounts or dates without writing a separate script for each document type.
A practical way to solve these problems is to use a modular document‑intelligence framework that combines layout detection, table structure recognition, OCR, word‑to‑block matching, and reading‑order reconstruction in a single pipeline. By configuring the analyzer with proven models—such as a DocLayNet‑based layout detector, a Table Transformer for table segmentation, and DocTR for OCR—you obtain page objects that expose text, bounding boxes, hierarchical relationships, and provenance information out of the box. Inspecting these objects lets you verify that figures are correctly linked to captions, tables are represented with rows, columns, and cell spans, and the narrative flow follows the visual layout.
When the built‑in outputs do not match your business needs, the framework allows you to register custom object types and insert your own pipeline component. For example, a small component can run regular expressions over the page‑level text to collect money and date mentions, compute the proportion of table area to decide whether a document is tabular, narrative, or mixed, and store these findings as page‑level summaries. Because the component is part of the pipeline, its results travel alongside the standard annotations and are available for filtering, serialization, or further processing.
Advanced users can assemble a pipeline manually with the framework’s service factory, swap in or out individual services, apply inbound filters to skip pages lacking tables, and even undo specific service effects to experiment with different configurations. Processed pages can be saved to JSON, reloaded to confirm that all structural annotations persist, and then flattened into JSONL records—each record containing a chunk of narrative text or a table’s HTML together with metadata like document id, page number, and reading order. These JSONL lines are ready to be ingested by retrieval systems or RAG pipelines, providing a clean, reproducible path from raw document to AI‑ready input.
By following this approach, teams eliminate the guesswork of stitching together disparate tools, gain fine‑grained control over each analysis step, and produce consistently structured output that feeds directly into downstream knowledge‑extraction and generative‑AI workflows.
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