ARXIV:2602.16430 · OCR DEPLOYMENT · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsErrorProof: failed

Designing Production-Scale OCR for India: Multilingual and Domain-Specific Systems

Q: What products could be built from this research?

The product could be a robust OCR service tailored for Indian markets, capable of handling diverse languages and document types efficiently.

Q: What are the practical use cases?

OCR solutions for digitizing Indian government and enterprise documents in multiple languages, enhancing efficiency in data processing and archiving.

Q: What industries could this research disrupt?

It replaces traditional OCR systems that are inefficient in terms of accuracy and latency when dealing with complex, multilingual documents.

arXiv

Multilingual, domain-specific OCR system for India's diverse documents with state-of-the-art results.

Blocked on Code›Score8.0Evidence failed

Opportunity summary

Pain Multilingual, domain-specific OCR system for India's diverse documents with state-of-the-art results.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence failed

Open Build Read PDF Signal Canvas Track

PROBLEM

Multilingual, domain-specific OCR system for India's diverse documents with state-of-the-art results. In this paper, we study two training strategies for building multilingual OCR systems with Vision-Language Models through the Chitrapathak series.

METHOD

Full abstract

Designing Optical Character Recognition (OCR) systems for India requires balancing linguistic diversity, document heterogeneity, and deployment constraints. In this paper, we study two training strategies for building multilingual OCR systems with Vision-Language Models through the Chitrapathak series. We first follow a popular multimodal approach, pairing a generic vision encoder with a strong multilingual language model and training the system end-to-end for OCR. Alternatively, we explore fine-tuning an existing OCR model, despite not being trained for the target languages. Through extensive evaluation on multilingual Indic OCR benchmarks and deployment-oriented metrics, we find that the second strategy consistently achieves better accuracy-latency trade-offs. Chitrapathak-2 achieves 3-6x speedup over its predecessor with being state-of-the-art (SOTA) in Telugu (6.69 char ANLS) and second best in the rest. In addition, we present Parichay, an independent OCR model series designed specifically for 9 Indian government documents to extract structured key fields, achieving 89.8% Exact Match score with a faster inference. Together, these systems achieve SOTA performance and provide practical guidance for building production-scale OCR pipelines in the Indian context.

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. Through extensive evaluation on multilingual Indic OCR benchmarks and deployment-oriented metrics, we find that the second strategy consistently achieves better accuracy-latency trade-offs.

WHY NOW

OCR Deployment moved forward this cycle; last verified April 2026. Public score 8.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score8.0

PainMultilingual, domain-specific OCR system for India's diverse documents with state-of-the-art results.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

Multilingual, domain-specific OCR system for India's diverse documents with state-of-the-art results.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsErrorProof: failed

ARXIV:2602.16430 · OCR DEPLOYMENT · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsErrorProof: failed

Designing Production-Scale OCR for India: Multilingual and Domain-Specific Systems

arXiv

Multilingual, domain-specific OCR system for India's diverse documents with state-of-the-art results.

Blocked on Code›Score8.0Evidence failed

Opportunity summary

Pain Multilingual, domain-specific OCR system for India's diverse documents with state-of-the-art results.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence failed

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

OCR Deployment moved forward this cycle; last verified April 2026. Public score 8.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score8.0

PainMultilingual, domain-specific OCR system for India's diverse documents with state-of-the-art results.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

Multilingual, domain-specific OCR system for India's diverse documents with state-of-the-art results.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsErrorProof: failed

Paper Pack

10.48550/arXiv.2602.16430

Designing Production-Scale OCR for India: Multilingual and Domain-Specific Systems

Multilingual, domain-specific OCR system for India's diverse documents with state-of-the-art results.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

failed

0 refs; 0 sources; 33% coverage.

What was readable

linkedon filenot materialized8 extracted35 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

8.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

OCR Deployment moved forward this cycle; last verified April 2026. Public score 8.0/10.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Through extensive evaluation on multilingual Indic OCR benchmarks and deployment-oriented metrics, we find that the second strategy consistently achieves better accuracy-latency trade-offs.
Implicationpartial
Directly stated in abstract with supporting evaluation results
Verificationpartial
partial
Evidencepartial
Chitrapathak-2 achieves 3-6x speedup over its predecessor with being state-of-the-art (SOTA) in Telugu (6.69 char ANLS)
Implicationpartial
Explicit numeric performance metrics provided in abstract
Verificationpartial
partial
Evidencepartial
achieving 89.8% Exact Match score with a faster inference
Implicationpartial
Explicit numeric performance metric provided in abstract
Verificationpartial
partial
Evidencepartial
may not easily adapt to languages or scripts not initially included
Implicationpartial
Directly stated in analysis caveats section
Verificationpartial
partial
Evidencepartial
The system relies on substantial initial data for training
Implicationpartial
Directly stated in analysis caveats section
Verificationpartial
partial
Evidencepartial
This research addresses a significant gap in OCR capability for the diverse and complex document landscape in India
Implicationpartial
Stated in analysis but requires some inference about significance
Verificationpartial
partial
Evidencepartial
The target market is Indian enterprises and government sectors requiring automated document digitization due to vast linguistic diversity.
Implicationpartial
Directly stated in product opportunity section of analysis
Verificationpartial
partial
Evidencepartial
and second best in the rest
Implicationpartial
Directly stated in abstract with clear performance ranking
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

OCR Deployment

Competitors

not indexed

Competitive landscape

Multilingual, domain-specific OCR system for India's diverse documents with state-of-the-art results.

Segment

OCR Deployment

Adoption evidence

No public code link in the paper record yet

Commercial read

8.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.16430 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(35)

IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs

2025Ali Faraz, Akash et al.

Seeing Straight: Document Orientation Detection for Efficient OCR

2025Suranjan Goswami, Abhinav Ravi et al.

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

2025Gheorghe Comanici, Eric Bieber et al.

Chitranuvad: Adapting Multi-lingual LLMs for Multimodal Translation

2025Shaharukh Khan, A. Tarun et al.

olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models

2025Jake Poznanski, Jon Borchardt et al.

Chitrarth: Bridging Vision and Language for a Billion People

2025Shaharukh Khan, A. Tarun et al.

Krutrim LLM: Multilingual Foundational Model for over a Billion People

2025Aditya Kallappa, Palash Kamble et al.

OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning

2024Ling Fu, Biao Yang et al.

OMNIPARSER: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition

2024Jianqiang Wan, Sibo Song et al.

Rotary Position Embedding for Vision Transformer

2024Byeongho Heo, Song Park et al.

Improved Baselines with Visual Instruction Tuning

2023Haotian Liu, Chunyuan Li et al.

Efficient Memory Management for Large Language Model Serving with PagedAttention

2023Woosuk Kwon, Zhuohan Li et al.

Nougat: Neural Optical Understanding for Academic Documents

2023Lukas Blecher, Guillem Cucurull et al.

End-to-end Document Recognition and Understanding with Dessurt

2022Brian L. Davis, B. Morse et al.

Transfer Learning for Scene Text Recognition in Indian Languages

2022Sanjana Gunna, Rohit Saluja et al.

TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models

2021Minghao Li, Tengchao Lv et al.

LoRA: Low-Rank Adaptation of Large Language Models

2021Edward J. Hu, Yelong Shen et al.

Learning Transferable Visual Models From Natural Language Supervision

2021Alec Radford, Jong Wook Kim et al.

ZeRO: Memory optimizations Toward Training Trillion Parameter Models

2019Samyam Rajbhandari, Jeff Rasley et al.

ASTER: An Attentional Scene Text Recognizer with Flexible Rectification

2019Baoguang Shi, Mingkun Yang et al.

Showing 20 of 35 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkAtlasOCR: Building the First Open-Source Darija OCR Model with Vision Language Models

8.0

Prior WorkMultimodal OCR: Parse Anything from Documents

8.0

Extension

Builds On ThisPP-OCRv5: A Specialized 5M-Parameter Model Rivaling Billion-Parameter Vision-Language Models on OCR Tasks

7.0

Builds On ThisEfficient Domain Adaptation for Text Line Recognition via Decoupled Language Models

7.0

Builds On ThisGLM-OCR Technical Report

7.0

Builds On ThisReading or Guessing? Visual Grounding Failures of Vision-Language Models for OCR in Ancient Greek Editions

2.0

Builds On ThisIndicDB -- Benchmarking Multilingual Text-to-SQL Capabilities in Indian Languages

7.0

Builds On ThisReliability-Oriented Multilingual Orthopedic Diagnosis: A Domain-Adaptive Modeling and a Conceptual Validation Framework

4.0

Builds On ThisA Robust Deep Learning Framework for Bangla License Plate Recognition Using YOLO and Vision-Language OCR

7.0

Builds On ThisMDPBench: A Benchmark for Multilingual Document Parsing in Real-World Scenarios

7.0

Commercially relevant

none indexed

Conflicting

none indexed

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.16430
Route: /paper/designing-production-scale-ocr-for-india-multilingual-and-domain-specific-systems
Active tab: read
Artifact: designing-production-scale-ocr-for-india-multilingual-and-domain-specific-systems

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/designing-production-scale-ocr-for-india-multilingual-and-domain-specific-systems/paper-pack
REST build passport API/api/v1/paper/designing-production-scale-ocr-for-india-multilingual-and-domain-specific-systems/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/designing-production-scale-ocr-for-india-multilingual-and-domain-specific-systems

degraded

Proof freshness: stale
Proof status: failed
Display score: 8/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

This page has proof data, but the latest verification did not complete cleanly.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Designing Production-Scale OCR for India: Multilingual and Domain-Specific Systems

Canonical ID designing-production-scale-ocr-for-india-multilingual-and-domain-specific-systems | Route /paper/designing-production-scale-ocr-for-india-multilingual-and-domain-specific-systems

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/designing-production-scale-ocr-for-india-multilingual-and-domain-specific-systems

MCP example

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source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Designing Production-Scale OCR for India: Multilingual and Domain-Specific Systems

/buildability/designing-production-scale-ocr-for-india-multilingual-and-domain-specific-systems

Watchwatch

Subject: Designing Production-Scale OCR for India: Multilingual and Domain-Specific Systems

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/designing-production-scale-ocr-for-india-multilingual-and-domain-specific-systems

Paper ref

designing-production-scale-ocr-for-india-multilingual-and-domain-specific-systems

arXiv id

2602.16430

Freshness

Generated at

2026-03-19T21:31:49.672Z

Evidence freshness

stale

Last verification

2026-03-19T21:31:49.672Z

Sources

References

Coverage

33%

Hash state

Lineage hash

a6deed851906e08afd19a54d471b9f6872a53c71efef43a1cff52b0d262c881a

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Designing Production-Scale OCR for India: Multilingual and Domain-Specific Systems

Designing Production-Scale OCR for India: Multilingual and Domain-Specific Systems

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