Custom data collection, expert annotation and off-the-shelf OCR datasets — invoices, receipts, handwriting, tables and multilingual documents — annotated to 99% accuracy* so your models read any document, in any language.
OCR training data is a labeled set of document images — scans, photos and PDFs of printed or handwritten text — paired with accurate text transcriptions and bounding-box annotations. Machine-learning models learn from this image-to-text data to turn documents into editable, machine-readable text.
Whether you’re fine-tuning a document-understanding model or building optical character recognition from scratch, the quality of your labeled data sets your accuracy ceiling. Shaip pairs domain-expert annotators with a proven Six Sigma QA process and a secure, compliant platform — delivering invoice, receipt, handwriting and multilingual OCR data your models can trust, at a fraction of the cost of building it in-house.
We source and collect document imagery to your exact spec — invoices, receipts, IDs, handwriting and multilingual scans — across real-world conditions and edge cases, compliantly in 60+ countries.
Character-, word- and row-level text transcription plus bounding-box, quadrilateral and polygon labeling by domain experts, with multi-round Six Sigma QA.
License ready-made, quality-checked OCR datasets today — handwriting recognition, receipt & invoice, table and multilingual document data — to prototype and benchmark fast.
Line items, totals, tax and vendor fields labeled for invoice OCR and receipt OCR — powering accounts-payable and expense automation.
Freestyle handwritten text datasets across many writers, styles and languages for ICR and cursive-recognition models.
Row, column and cell-level extraction from complex tables and structured tabular documents.
Passports, licenses and ID cards with key-value annotation for identity verification and KYC.
Flight tickets, applications and structured forms with field-level labeling and key-value extraction.
Signage, labels and product text captured in natural, real-world image conditions.
Off-the-shelf catalog
Pre-built, quality-checked OCR datasets you can license today — handwriting, receipt & invoice, table, multilingual and scene-text data — or use them as a starting point for a custom OCR dataset build.
5k videos of barcodes with a duration of 30-40 sec from multiple geographies
Use Case: Object Recognition Model
Format: Videos
Volume: 5,000+
Annotation: No
15.9k images of receipts, invoices, purchase orders in 5 languages i.e. English, French, Spanish, Italian & Dutch
Use Case: Doc. Recognition Model
Format: Images
Volume: 15,900+
Annotation: No
Delivered 45k images of German & UK Invoices
Use Case: Invoice Recog. Model
Format: Images
Volume: 45,000+
Annotation: No
3.5k images of Vehicle License Plates from different angles
Use Case: No. Plate Recognition
Format: Images
Volume: 3,500+
Annotation: No
Collected and annotated 90K documents in English, French, Spanish, German, Italian, Portuguese and Korean
Use Case: OCR Model
Format: Images
Volume: 90,000+
Annotation: Yes
23.5k docs in Japanese, Russian & Korean languages from Signs, Storefronts, Bottles, Documents, Posters, Flyers.
Use Case: Multilingual OCR Model
Format: Images
Volume: 23,500+
Annotation: Yes
11.5k+ images of receipt from major European cities
Use Case: Object detection model
Format: Images
Volume: 11,500+
Annotation: No
75k+ receipts in multiple languages
Use Case: Receipt AI Models
Format: Images
Volume: 75,000+
Annotation: No
By Industry
Prescription, lab-report and medical-form datasets — HIPAA-compliant handling for clinical document AI.
Invoice, bank-statement, cheque and KYC-document data for financial document data extraction.
Consignment notes, delivery slips and bills of lading for shipping and freight automation.
Claims forms, policy documents and handwritten submissions labeled at scale.
Receipt, price-tag and shelf-label data for expense, loyalty and merchandising AI.
Contract, record and archival-scan digitization datasets for document processing programs.
Our Process
How is OCR accuracy achieved? Through the right annotation types plus multi-round human QA. We label at character, word and row level using bounding-box, quadrilateral and polygon annotation, then verify every batch before delivery — targeting 99% accuracy.*
Define document types, languages and edge cases; source or collect compliant document imagery.
Character/word/row text transcription + bounding-box, quad and polygon labeling by trained experts.
Independent review and Six Sigma checks measure Character & Word Error Rate against your SLA.
Ship in your model-ready format; refine on feedback and expand dataset coverage over time.
Shaip built an end-to-end OCR annotation pipeline — word-level bounding boxes with character-level transcription across 10+ text sources, tackling curved signage, faded receipts, handwriting, and mixed scripts. Five attribute layers and dual QA delivered production-ready datasets at 99% accuracy.
Why Choose Shaip
Shaip has spent years sourcing, collecting and annotating AI training data across every modality. For optical character recognition, that means depth you can’t build overnight — global reach, domain expertise, enterprise-grade security and proprietary tooling behind every OCR dataset.
Data sourced and curated from 60+ countries in 65+ languages across 70+ topics — coverage most vendors can't match.
30,000+ trained annotators and industry specialists deliver precise, context-aware OCR annotation, not generic labeling.
GDPR, HIPAA, CCPA, ISO 9001:2015, ISO 27001 and SOC 2 Type II — with NDAs and secure data handling end to end.
Shaip Manage, Shaip Work and Shaip Intelligence run project oversight, global workforce coordination and automated data validation.
Get quality data at a fraction of the cost of building it yourself — and get your models to market faster.
Six Sigma QA and measurable accuracy targets (Character & Word Error Rate) mean data you can trust in production.
OCR is a technology that allows machines to read printed text and images. It is often used in business applications, such as digitizing documents for storage or processing, and in consumer applications, such as scanning a receipt for expense reimbursement.
The healthcare industry faces a paradigm shift in its workflows with the inception of new and advanced technologies in AI. Leveraging AI tools and technologies, improved medical outcomes can be acquired with higher healthcare efficiency.
Ever scratched your head, amazed at how Google or Alexa seemed to ‘get’ you? Or have you found yourself reading a computer-generated essay that sounds eerily human? You’re not alone. It’s time to pull back the curtain and reveal the secret: Large Language Models, or LLMs.
OCR, or Optical Character Recognition, is a technology that converts printed or handwritten text in images or scanned documents into machine-readable text. It works by training AI models with labeled datasets to recognize patterns and characters in diverse formats like receipts, invoices, and forms.
OCR is vital for automating tasks like document processing, data extraction, and digitization. It helps businesses save time, reduce errors, and improve efficiency in handling large volumes of physical or scanned documents.
Machine learning enhances OCR by training models with diverse datasets, enabling them to handle variations in fonts, handwriting styles, layouts, and languages. Over time, the models learn to generalize and improve recognition rates.
OCR can process a wide range of documents such as receipts, invoices, handwritten forms, passports, medical labels, tickets, and even complex tables in scanned PDFs or images.
Table OCR extracts structured data from tables in scanned documents, PDFs, or images. It converts rows and columns into machine-readable formats like Excel, making data processing faster and more accurate.
OCR is widely used in industries like healthcare, finance, and eCommerce. It automates data extraction from medical records, invoices, receipts, and other documents, improving operational efficiency across sectors.
Multilingual OCR models are trained with datasets covering various languages, dialects, and font styles. This allows them to accurately recognize and process text across different scripts and typography.
Training OCR models involves handling diverse handwriting, fonts, layouts, and languages. Ensuring accuracy in recognizing complex documents like medical receipts or multilingual content is also a key challenge.
Shaip offers high-quality, client-specific OCR datasets, including receipts, invoices, handwritten forms, and multilingual documents. These datasets are curated, annotated, and validated to ensure maximum accuracy and reliability.
Shaip’s OCR training solutions are highly scalable and designed to deliver exceptional accuracy. Their process combines advanced AI tools with human expertise, ensuring reliable results even with large datasets.
The cost depends on the type, volume, and complexity of the dataset required. For customized pricing, businesses can contact Shaip directly to discuss their specific needs.
