Documents may be significantly degraded, including physical document damage, poor scanning, and poor image quality Formats can vary significantly, and may contain complex structures which can confuse off-the-shelf software
"ATYETI BUILDS SCALABLE COMPLEX LEARNING SYSTEMS FOR LARGE SCALE DOCUMENT EXTRACTIONS. HIGH ACCURACY IS ACHIEVABLE DESPITE DEGRADATION, HANDWRITING, AND VARIABLE FORMATS."
Clients and Industries:
- Credit Suisse
- Patent River
- Anaqua
- KYC Automation, Prosecution Analytics, Prosecution Simulation, Language Modeling, Identity Resolution
Business Problem
- Scanned documents contain information locked in an image and require a recognition and analysis processes in order to extract that information reliably for business use.
- Legal documents hand-signed and then scanned
- Faxes
- Hand-filled forms
- Physical mail
Recognition
- Custom-tuned deep learning models designed specifically for the subject
- Scalable, on-demand parallel processes for large volumes
- Custom entity definitions, static and variable formats, and accommodation for complex or unknown page layouts
Analysis
- Contextual analysis helps refine and validate extraction
- Mixing tools like ML, RegEx, and expert rule systems allows multiple levels at which data errors can be corrected or raised
- Targeted pri orities, information type frequencies, and error tracking allow analysts to tune the process and achieve high levels of accuracy
Result : Large Scale, Accurate Results
- Atyeti systems have extracted data from over 2 billion documents
- Deep NLP systems perform multiple tasks including complex entity recognition, conversational mapping, and data verification
- Our computer vision systems provide handwriting recognition, figure extraction, feature identification, figureto-text mapping and document de-degradation