Optical Character Recognition
AI Training Data For OCR
Optimize data digitization with high-quality Optical Character Recognition (OCR) training data to build intelligent ML models.
Reduce the learning curve of AI models with reliable OCR Training Dataset
Deciphering and digitizing scanned images of text is a challenge for many businesses developing reliable AI and Deep Learning models. With Optical Character Recognition, a specialized process, it is possible to search, index, extract and optimize data into machine-readable format. This scanned document dataset is being used to extract information from handwritten documents, invoices, bills, receipts, travel tickets, passports, medical labels, street signs and more. To develop reliable and optimized models, it should be trained on OCR datasets that have extracted data from thousands of scanned documents.
How our expertise in developing accurate OCR training datasets works in YOUR favor?
• We provide client-specific OCR training dataset solutions that help customers develop optimized AI models.
• Our capabilities extend to offering scanned PDF datasets and covering different letter sizes, fonts and symbols from documents.
• We combine the precision of technology & human experience to provide a scalable, reliable and affordable solution for clients.
OCR Use Cases
Freestyle handwritten text datasets to develop powerful ML models.
Collect / Source thousands of high-quality handwritten datasets in hundreds of languages and dialects to train machine learning (ML) and deep learning (DL) models. We can also help in extracting text within an image.
Handwritten Forms Dataset
Freestyle Handwritten Text Paragraphs Datasets
Receipt/Invoice
Datasets consisting of invoice/ receipt where several items were purchased e.g., coffee shop, Restaurant bills, Grocery, Online shopping, Toll receipts, Airport cloakroom, Lounge, Fuel bill, Bar invoice, internet bills, shopping bills, taxi receipts, restaurant bills, etc. collected from different region and in different languages as required for the ML model. Save significant time and money by transcribing key data from invoices and receipts effectively and accurately.
Receipt Data Collection: Data Extraction of Receipts with OCR
Invoice Data Collection: Transcribe reliable data with Scanned Invoice Datasets
Tickets: Flight tickets, Taxi tickets, Parking ticket, Train tickets, Movie Ticket Processing with OCR
Transcription of Multi-category Scanned Documents: Newsletters, Resume, Forms with checkbox, Multi-document in a single image, User manual, Tax forms etc.
Multilingual Document
Multilingual handwritten data collection services for pattern recognition, computer vision, and other machine learning solutions to train Optical Character Recognition models.
OCR – Multilingual document 1
OCR – Multilingual document 2
Scene Data Collection
Medicine bottle with labels, English Street/Road scene with car license plate, English Street/Road scene with instruction/info board etc.
Transcribe Medical Labels or Drug Labels with OCR
Number Plate Recognition using OCR
Detecting Street/Road & Extract Information Street Board data with OCR
OCR Datasets
Text & Image Optical Character Recognition (OCR) Datasets to get you going in order to train real-world applications. Can’t find the data you need? Contact Us Today.
Barcode Scanning Video Dataset
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
Invoices, PO, Receipts Image Dataset
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
German & UK Invoice Image Dataset
Delivered 45k images of German & UK Invoices
- Use Case: Invoice Recog. Model
- Format: Images
- Volume: 45,000+
- Annotation: No
Vehicle License Plate Dataset
3.5k images of Vehicle License Plates from different angles
- Use Case: No. Plate Recognition
- Format: Images
- Volume: 3,500+
- Annotation: No
Handwritten Document Image Dataset
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
Document Dataset for OCR
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
European Receipt Image Dataset
11.5k+ images of receipt from major European cities
- Use Case: Object detection model
- Format: Images
- Volume: 11,500+
- Annotation: No
Invoice/Receipt Dataset
75k+ receipts in multiple languages
- Use Case: Receipt AI Models
- Format: Images
- Volume: 75,000+
- Annotation: No
Featured Clients
Empowering teams to build world-leading AI products.
Our Capability
People
Dedicated and trained teams:
- 30,000+ collaborators for Data Collection, Labeling & QA
- Credentialed Project Management Team
- Experienced Product Development Team
- Talent Pool Sourcing & Onboarding Team
Process
Highest process efficiency is assured with:
- Robust 6 Sigma Stage-Gate Process
- A dedicated team of 6 Sigma black belts – Key process owners & Quality compliance
- Continuous Improvement & Feedback Loop
Platform
The patented platform offers benefits:
- Web-based end-to-end platform
- Impeccable Quality
- Faster TAT
- Seamless Delivery
Recommended Resources
Infographics
OCR – Definition, Benefits, Challenges, and Use Cases
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.
Blog
OCR in Healthcare: A Comprehensive Guide to Use Cases, Benefits
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.
Buyer’s Guide
Buyer’s Guide for Large Language Models LLM
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.
Let’s discuss your OCR Training Data needs today
Frequently Asked Questions (FAQ)
OCR refers to a technology that enables computers to recognize and convert printed or handwritten characters in images or scanned documents into machine-encoded text. Machine learning models are often employed to enhance the accuracy and adaptability of OCR systems.
OCR works by using labeled datasets consisting of images of text and their corresponding digital transcriptions. The model is trained to recognize patterns in these images that correspond to specific characters or words. Over time, with enough data and iterative training, the model improves its accuracy in character recognition.
OCR is crucial in ML model training because it allows the model to learn and generalize from diverse textual representations, making it adaptable to various fonts, handwritings, and document types. A well-trained OCR model can handle real-world variances in text, resulting in more accurate text recognition across various applications.
Businesses can leverage OCR (Optical Character Recognition) technology to automate data entry from physical documents, digitize and search paper archives, efficiently process invoices and receipts, automatically extract information from forms, convert scanned PDFs into searchable formats, integrate with mobile apps for on-the-go data capture, and verify and authenticate documents in sectors like banking. Through these applications, OCR helps streamline operations, reduce manual errors, and enhance digital accessibility.