Comparisons

NETRA vs AWS Textract vs Google Document AI: Which Is Best for Indian Documents?

Data-driven comparison of NETRA, AWS Textract, and Google Document AI for Indian document processing — accuracy, language support, pricing, and benchmarks.

31 March 202612 min readBy Anumiti Team

Choosing a document AI service for Indian document processing is a decision with long-term implications for accuracy, cost, and compliance. The three leading options — NETRA by Anumiti, AWS Textract by Amazon, and Google Document AI — take fundamentally different approaches to the problem. This comparison provides concrete data to help engineering and product teams make an informed choice.

All three services were benchmarked across the same test corpus: 5,000 Indian documents spanning GST invoices, PAN cards, Aadhaar cards, bank statements, driving licenses, and court orders, in Hindi, Tamil, Bengali, Telugu, Kannada, and English. Results were verified by human reviewers.

How Do These Three Services Compare on Core Features?

The feature comparison below covers the 18 dimensions most relevant to Indian document processing. Each feature was evaluated through direct API testing, documentation review, and production deployment experience.

The fundamental difference is architectural: AWS Textract and Google Document AI are general-purpose document services adapted from global platforms, while NETRA was built specifically for Indian documents. This shows most clearly in language support, document-type understanding, and India-specific compliance features.

| GSTIN validation | Built-in format + network check | No | No |

What Accuracy Do They Achieve on Common Indian Documents?

Accuracy is the most critical metric for document processing. A 5% accuracy gap might seem small, but on a field like GSTIN (15 characters), 5% field-level error means 1 in 20 GSTINs extracted incorrectly — enough to cause ITC mismatches, failed e-invoice generation, and compliance notices.

The benchmarks below measure field-level accuracy — the percentage of individual fields (name, number, date, amount) extracted correctly, not just character-level text recognition. A field is counted correct only if the complete extracted value matches the ground truth exactly.

PAN Card Extraction (1,000 documents tested)

| PAN Number | 98.2% | 91.4% | 93.7% |

| Name (English) | 97.5% | 94.1% | 95.2% |

| Name (Hindi) | 96.1% | 72.3% | 84.6% |

| Father's Name | 95.8% | 70.8% | 82.1% |

| Date of Birth | 97.9% | 93.5% | 95.0% |

| Overall field accuracy | 97.1% | 84.4% | 90.1% |

Note: Textract and Document AI do not have PAN card processors, so results include custom post-processing logic to extract PAN-specific fields from raw OCR output. NETRA returns structured PAN fields natively.

GST Invoice Extraction (1,500 documents, mixed Hindi/English/Tamil)

| Supplier GSTIN | 97.8% | 88.2% | 90.5% |

| Invoice Number | 96.5% | 91.7% | 93.2% |

| Invoice Date | 98.1% | 94.3% | 95.8% |

| Line Item HSN | 94.2% | 68.5% | 72.1% |

| Line Item Description (Hindi) | 93.1% | 55.4% | 74.8% |

| Line Item Amount | 96.7% | 89.3% | 91.5% |

| Tax Amounts (CGST/SGST/IGST) | 95.9% | 85.7% | 88.2% |

| Place of Supply | 96.3% | 42.1% | 48.5% |

| Overall field accuracy | 96.1% | 76.9% | 81.8% |

*Place of Supply is an India-specific field that generic invoice parsers do not explicitly extract. The low scores reflect the post-processing attempting to identify this field from raw output.

Aadhaar Card Extraction (800 documents, 8 regional languages)

| Aadhaar Number | 97.5% | 90.2% | 92.8% |

| Name (English) | 96.8% | 92.5% | 94.1% |

| Name (Regional) | 94.2% | 58.7% | 71.3% |

| Date of Birth | 97.1% | 91.8% | 93.5% |

| Gender | 98.5% | 95.2% | 96.1% |

| Address (English) | 93.5% | 84.3% | 87.2% |

| Address (Regional) | 91.2% | 45.6% | 62.8% |

| Overall field accuracy | 95.5% | 79.8% | 85.4% |

The pattern is consistent: all three services perform well on English text in standardized positions, but NETRA maintains accuracy on regional language fields and India-specific data points where the other services degrade significantly.

How Does Latency Compare for Real-Time Processing?

Latency matters for user-facing applications — KYC onboarding, point-of-sale invoice processing, and mobile document scanning. The table below shows median and P95 latencies measured from an application server in Mumbai (AWS ap-south-1 region).

| PAN Card (single page) | 65ms / 95ms | 1.2s / 2.8s | 0.9s / 1.9s |

| GST Invoice (single page) | 78ms / 110ms | 1.5s / 3.2s | 1.1s / 2.4s |

| GST Invoice (3 pages) | 185ms / 280ms | 3.8s / 6.5s | 2.8s / 4.7s |

| Bank Statement (5 pages) | 320ms / 510ms | 5.2s / 9.1s | 3.9s / 6.8s |

| Court Order (10 pages) | 680ms / 1.1s | 9.5s / 15.2s | 7.2s / 12.1s |

NETRA's latency advantage is 10-15x for single-page documents and 8-12x for multi-page documents. This is primarily due to three factors: India-hosted processing infrastructure (zero cross-region latency), document-type-specific models that are smaller and faster than general-purpose models, and an inference pipeline optimized for the specific field-extraction task rather than general document understanding.

For applications requiring sub-200ms response times (mobile KYC, POS invoice scanning), NETRA is the only option that consistently meets this threshold without edge-side caching.

How Does Pricing Compare at Different Volumes?

Cost analysis must account for the total cost of extracting usable, structured data — not just the per-page API price. Generic services often require significant post-processing development and compute to match purpose-built extraction output.

| 1,000 pages | ₹1,500 | ₹1,875 | ₹1,650 |

| 10,000 pages | ₹12,500 | ₹18,750 | ₹15,000 |

| 50,000 pages | ₹50,000 | ₹93,750 | ₹75,000 |

| 100,000 pages | ₹85,000 | ₹1,87,500 | ₹1,45,000 |

| 500,000 pages | ₹3,50,000 | ₹9,37,500 | ₹7,25,000 |

*AWS Textract and Google Document AI costs include the AnalyzeDocument/Forms+Tables tier pricing, converted at ₹83/USD. These costs do not include the additional compute cost of running custom post-processing logic to extract India-specific fields, which typically adds 20-40% to the effective per-page cost.

Hidden costs with generic services:

1. Post-processing development. Building custom logic to extract PAN numbers, GSTINs, HSN codes, and other India-specific fields from raw Textract/Document AI output requires 2-4 weeks of engineering effort and ongoing maintenance as document formats change.

2. Accuracy-driven rework. Lower accuracy on Indic scripts means more documents routed to human review. At 80% accuracy, 1 in 5 documents needs manual correction. At 96% accuracy, only 1 in 25 does. The labor cost difference compounds at scale.

3. Compliance overhead. Managing DPDP compliance separately from document processing adds architectural complexity. With NETRA, data residency and retention policies are built into the processing pipeline.

When Should You Use Each Service?

The right choice depends on your specific requirements across five dimensions: language needs, document types, accuracy requirements, existing infrastructure, and budget constraints. Here is a decision framework.

Choose AWS Textract when:

Your documents are primarily in English

You are already deeply invested in the AWS ecosystem (S3, Lambda, Step Functions)

You need to process global document formats (US tax forms, EU invoices) alongside some Indian documents

Your team has the engineering capacity to build and maintain India-specific post-processing

Latency is not a critical requirement (batch processing is acceptable)

Choose Google Document AI when:

You need strong general-purpose OCR with decent Hindi support

Your infrastructure is on Google Cloud Platform

You process a mix of Indian and international documents

You want access to Google's broader AI ecosystem (Vertex AI, BigQuery)

You are comfortable with good-enough accuracy on regional languages and can supplement with human review

Choose NETRA when:

Your documents are primarily Indian (GST invoices, PAN, Aadhaar, bank statements, legal documents)

You need accurate extraction across multiple Indic scripts (not just Hindi)

Sub-100ms latency is required for user-facing flows

India data residency is a hard requirement (DPDP, RBI, SEBI regulations)

You want structured, document-type-specific output without building custom post-processing

Cost-effectiveness at scale is important

You need integrated GSTIN/PAN validation alongside extraction

How Do You Migrate from Textract or Document AI to an India-Optimized Service?

If you have an existing integration with Textract or Document AI and are considering migration, the process is structured around three phases: audit, parallel run, and cutover.

Phase 1: Audit (1-2 days). Inventory your current document types, volumes, and the post-processing logic you have built. Identify which custom code extracts India-specific fields (GSTIN parsing, PAN extraction, HSN lookup). This code is likely replaceable with native extraction fields. Phase 2: Parallel run (1-2 weeks). Process a representative sample of documents through both your current service and the new one. Compare extraction results field-by-field. Measure accuracy, latency, and cost differences on your actual document distribution — not synthetic benchmarks. Pay special attention to your hardest document types (low-quality scans, handwritten annotations, complex multi-page invoices). Phase 3: Cutover (1-3 days). Map the new API's response schema to your application's data model. Because NETRA returns richer, typed fields, you will likely simplify your integration code — removing post-processing logic that was needed to structure raw OCR output. Update your error handling for the new API's error codes and rate limits.

```python

# Example: Mapping Textract output to your schema (BEFORE)

def extract_gstin_from_textract(textract_response):

"""Custom post-processing to find GSTIN in Textract output."""

import re

gstin_pattern = r"[0-9]{2}[A-Z]{5}[0-9]{4}[A-Z]{1}[1-9A-Z]{1}Z[0-9A-Z]{1}"

for block in textract_response["Blocks"]:

if block["BlockType"] == "LINE":

match = re.search(gstin_pattern, block["Text"])

if match:

return match.group(0)

return None # GSTIN not found — common failure mode

# Example: Direct field access with NETRA (AFTER)

def extract_gstin_from_netra(netra_response):

"""GSTIN is a first-class field in the response."""

return netra_response["data"]["supplier"]["gstin"]

# Also available: confidence score, validation status, state name

```

For detailed migration guides, see our comparison pages for NETRA vs Textract and NETRA vs Document AI.

What Does the Future of Indian Document AI Look Like?

The Indian document processing landscape is evolving rapidly across three vectors: regulatory requirements, technology capabilities, and market adoption.

Regulatory push. The government's digitization initiatives — e-invoicing threshold expansion (CBIC has lowered it from ₹500 crore to ₹5 crore, with further reduction expected), mandatory faceless assessments, DigiLocker integration for document verification, and DPDP Act compliance requirements — are creating non-optional demand for automated document processing. Organizations that invest in robust extraction pipelines now will have a structural advantage as these mandates expand. Technology convergence. Vision-language models are collapsing the traditional OCR pipeline (image processing, text recognition, layout analysis, field extraction) into unified models that understand documents holistically. For Indian documents, this means models that can read a Tamil GST invoice, understand the tax structure, and validate the arithmetic — all in a single inference pass. This is the approach NETRA takes, and it explains both the accuracy and latency advantages. Market scale. India has 14.2 million active GST registrations as per GSTN data, 63 million MSMEs, and over 1.4 billion Aadhaar holders. The volume of documents requiring automated processing will only grow as India's digital economy expands. The market is moving from early-adopter phase (large enterprises and fintechs) to mainstream adoption (mid-market companies and MSMEs), driven by affordability and regulatory compliance.

The choice of document AI service you make today will compound over months and years as your document volumes grow, regulatory requirements tighten, and the accuracy gap between India-optimized and generic solutions becomes harder to bridge with custom engineering.

NETRATextractDocument-AIcomparisonOCRIndian-documents

NETRA vs AWS Textract vs Google Document AI: Which Is Best for Indian Documents?

How Do These Three Services Compare on Core Features?

What Accuracy Do They Achieve on Common Indian Documents?

How Does Latency Compare for Real-Time Processing?

How Does Pricing Compare at Different Volumes?

When Should You Use Each Service?

How Do You Migrate from Textract or Document AI to an India-Optimized Service?

What Does the Future of Indian Document AI Look Like?

Frequently Asked Questions

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