Invoice field extraction
Challenge
Invoice templates place the same information in different locations.
Our approach
Label supplier, date and line-item fields with normalized field names.
Introducing advanced AI data pipelines for Documents
Turn complex documents into structured training data
Invoices, forms, reports and scanned archives need labels that capture real operational detail. Engai prepares documents datasets around your objects, scenes and review requirements.
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Documents Data Annotation
Expert Labeling of Document
Data
Document Annotation services for a wide range of computer vision applications.
0
completed projects
Invoice & Form Extraction
Documents · OCR & Bounding Boxes
0
annotated files
Field-Level Precision
We employ in-house teams to help you annotate a variety of documents, such as invoices, cheques, bills, and even newspapers and magazines. All paper documents are different. If you need to train your computer vision model to read them all, we are here to help.
This doesn't just apply to paper - computer vision can learn to read text in different environments such as billboards, license plates, road signs, and so on. Any company that trains AI for real-world applications can benefit from accurate document annotation.
Documents / Capabilities
Documents Core Solutions
Carefully annotated data for documents perception, automation and research.
Documents label taxonomy
Define classes for text regions, fields and table cells, with visual examples and explicit boundary rules.
Capture-aware quality review
Review the effects of lighting, scale and occlusion in invoices, forms, reports and scanned archives.
Model-ready annotation data
Connect documents labels to source identifiers, scene metadata and the export schema your model uses.
Annotation capabilities
Documents Data Services
Invoices, forms, reports and scanned archives need labels that capture real operational detail. Engai prepares documents datasets around your objects, scenes and review requirements.
Locate each visible object with an axis-aligned rectangle and a class label. Applied to text regions, fields and table cells.
Data Annotation for Documents
Invoice OCR automation
Label supplier details, dates and line items while preserving links between extracted text and source regions.
Intelligent document processing
Segment headings, paragraphs, tables and footnotes into a consistent layout hierarchy.
Structured data extraction
Map form fields and table relationships to stable schemas across varied document templates.
Document classification
Organize reports, forms and correspondence by agreed categories and representative edge cases.
Searchable archive preparation
Annotate reading order and document structure to support retrieval from scanned collections.
Client reviews
on 
Careful work on the small details
The sample batch helped us agree on how to handle overlapping leaves and partially visible crops.
Read more
Having those edge cases discussed early would give our team a clearer baseline before scaling annotation.
Clear communication throughout
Our labeling requirements evolved as we reviewed the imagery. The feedback process felt straightforward.
Read more
A shared set of examples and regular checkpoints would help keep the dataset consistent across batches.
A practical approach to field data
Field images are rarely perfect. We appreciated the attention to shadows, occlusion and changes in lighting.
Read more
These are the details we would want a labeling partner to consider when preparing data for crop-monitoring models.
Documents in practice
Annotation applications across documents workflows.
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Invoice field extraction
THE CHALLENGE
Invoice templates place the same information in different locations.
THE SOLUTION
Label supplier, date and line-item fields with normalized field names.
DATA DELIVERABLE
Reviewed field extraction data with a consistent label schema and documented edge cases.
Invoice field extraction
+15%
Throughput target
A project target for improving throughput with task-specific training data.
Sub-CM
Resolution target
Where calibrated source imagery supports fine spatial measurements.
40%
Efficiency target
A project target for reducing review effort through assisted labeling.
Deployment options
Build your workflow around the infrastructure your team uses.
On-Premises
Secure local processing for sensitive datasets.
Cloud
Highly scalable infrastructure powered by AWS/GCP.
Edge
Real-time inference optimized for on-device hardware.
Formats & platforms
ONNX
TensorRT
Core ML
OpenVINO
LiteRT
TorchScript
PaddlePaddle
IMX500
Qualcomm
Huawei
Available formats depend on your model, runtime and target hardware.
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Live Infrastructure Telemetry
1.2BData Points
189Models
24Pipelines
3Alerts
TypeOriginOwnerStatus
Vision ModelGitHubDave DigsterHealthy
DatasetAWS S3Emily ThompsonSyncing
DeploymentGCPRobert BrownActive
Zero-Trust Dataset Confidentiality
Your requirements and proprietary sample assets are encrypted in transit and never used for public model training.