Palmadoc
LANCER
Lancer :
Ideal for quick, focused engagements
Team: ≤ 2 resources
Duration: ≤ 3 weeks
Indicative Budget: ≤ $3,000 USD*
Use Cases: MVPs, prototypes, simple websites, bug-fix sprints
Indicative Pricing: ~$15–$25/hr per resource*
Growth :
Best suited for growing products or structured builds
Team: 2–5 resources
Duration: Up to 4 months
Indicative Budget: Up to $30,000 USD*
Use Cases: Feature-rich web apps, integrations, custom dashboards
Indicative Pricing: ~$15–$35/hr per resource*
Enterprise :
Tailored for high-scale, complex or long-term initiatives
Team: 5+ resources
Duration: More than 4 months
Indicative Budget: Starts from $30,000+ USD*
Use Cases: End-to-end platforms, SaaS products, compliance-heavy systems
Indicative Pricing: ~$25–$50/hr depending on roles*
(Development)
Project Description
A digital solutions provider focused on streamlining financial workflows approached Oodles to automate invoice data extraction across varying formats like PDF, JPG, and PNG. Their goal was to reduce manual entry and improve data accuracy. The client sought a scalable OCR solution powered by ML and NLP to extract and classify invoice fields with precision.
Scope Of Work
The project focused on automating invoice content extraction and classification to reduce human error and processing time. The client required an intelligent system with the help of Oodles that could parse image-based and PDF invoices using OCR, integrate ML models, and classify entities like dates and invoice numbers. Areas of work included data preprocessing, model development, OCR integration, NLP, and active learning.
Our Solution
To address the client’s challenges, a multi-phase strategy was implemented over 10 sprints with clear delivery goals. The solution combined image processing, machine learning, and NLP to build a document parsing pipeline tailored to unstructured invoice data.
Key Features Implemented:
- OCR Setup & Integration: Used Tesseract-OCR to extract text from scanned or image-based invoices.
- ML Model Development: Trained supervised models to identify invoice elements and optimized them for accuracy using hyperparameter tuning.
- Image Enhancement Workflow: Applied image resizing and contrast adjustment to improve OCR results.
- NLP-Based Entity Classification: Implemented NLP extractors to detect and classify fields like invoice number, vendor details, amount, and dates.
- Active Learning Module: Enabled continuous learning by feeding verified outputs back to the model for retraining and refinement.
Share Your Requirements