Layout-Aware Document Intelligence Platform Universal Structured Data Extraction

Layout-Aware Intelligence

Conventional OCR fails on complexity. Nishkar understands document architecture before reading content—mimicking cognitive structural understanding to extract structured data from non-linear layouts where traditional systems collapse into noise.

Precision Metric

99.2 %

Accuracy at high density

Initial Benchmark

English

Scaling to 22+ Languages

★

"The most authoritative data recovery suite we've deployed this decade."

BREAKING: NISHKAR ARCHITECTURE DEFINED: THE SIEVE, THE PICK, AND THE FORGE.

DOC-INTELLIGENCE: HANDLING MULTI-COLUMN, NESTED TABLES, AND TECHNICAL DIAGRAMS.

SCALING: VALIDATED ON ENGLISH L1, EXPANDING TO 22+ LANGUAGES.

The Linear Constraint of Legacy Systems

Traditional OCR assumes a flat, top-to-bottom reading order. They fail catastrophically on non-linear layouts—multi-column reports, interleaved forms, and nested tables—flattening structured information into unusable text streams.

01 /

Structural Destruction

Hierarchy is collapsed into linear strings, breaking data integrity.

02 /

Contextual Fragmentation

Fields are detached from descriptors, leading to extraction failure.

Archive: Legacy Output [Unstructured]

Invoice #8822 | Date: 2024-01-15 | Total: 1,250.00 | Desc: Consulting Services... CRITICAL FAILURE: FIELD HIERARCHY NOT DETECTED

Live Feed: Nishkar Extraction [Structured]

{
  "header": {
    "doc_id": "NV-8822",
    "timestamp": "2026-02-04"
  },
  "extraction": {
    "entity": "NISHKAR BUREAU",
    "validity": "VERIFIED",
    "confidence": 0.998
  }
}

Capabilities / Overview

Cognitive Precision

Structural Mapping

Advanced spatial logic that identifies document topography. Nishkar maps columns, headers, and nested forms with sub-pixel precision.

Structural Understanding

Handling multi-industry layouts where traditional OCR fails. English-first validation with roadmap to 22+ Indian languages.

Immutable Accuracy

Real-time validation against known benchmarks. Our engine iteratively refines confidence scores to achieve 99% baseline accuracy.

Standard Operating Procedure

The Refinery Process

Live Simulation: Pipeline Core

THE TIMES...

RECOVERY...

STRUCTURAL...

[ 01 ] THE SIEVE

DocLayout-YOLO identifies topography, isolating tables and columns before interpretation.

[ 02 ] THE PICK

Region-specific OCR using LightonOCR-2 1B for 90%+ character precision.

[ 03 ] THE CRUCIBLE

Multilingual NER (IndicBERT) converts raw text into semantic entities and relations.

[ 04 ] THE FORGE

Final export to Neo4j knowledge graphs, preserving document provenance.

Module: The Sieve

Layout Analysis Engine

DocLayout-YOLO identifies document topography, isolating tables, margins, and nested registries before a single character is interpreted. Preserving reading order and spatial hierarchy.

[ mAP 85.0+ ] [ SPATIAL AWARE ]

Topography Map L1

Module: The Pick

Region-Specific OCR

LightonOCR-2 1B integration for high-accuracy text extraction per segmented region. Handles multi-column, rotated text, and nested tables with 90%+ precision.

[ CER < 8% ] [ ENGLISH L1 ]

Extracted Stream L2

Module: The Crucible

Knowledge Refinement

IndicBERT and spaCy power our multilingual Named Entity Recognition (NER), identifying leaders, locations, and events while resolving semantic relationships.

[ F1 0.82 ] [ MULTILINGUAL ]

Semantic Map L3

Module: The Forge

Structured Knowledge Graph

Neo4j construction of temporal knowledge graphs. Connecting facts to publication dates, preserving original document provenance for source-critical research.

[ NEO4J ] [ GRAPH READY ]

Structured Registry L4