Sikuli OCR- Ashwak Kamal
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 US-based automation solutions provider with expertise in cross-platform scripting engaged Oodles to streamline its document recognition and screen automation workflows. The client required an OCR-enabled system that could handle browser-based UI interactions and desktop automation through visual input, integrated with headless execution environments.
Scope Of Work
The client sought Oodles for a robust Java-based framework that could recognize on-screen data using OCR and perform GUI automation across platforms. The project involved automating form interactions, enabling screen scraping, and integrating cross-platform execution support using Sikuli, Tesseract, XVFB, and related technologies.
Our Solution
To address the client’s goals, a comprehensive automation suite was developed using a combination of OCR, computer vision, and screen automation tools.
Key highlights include:
- Visual UI Recognition & Automation: Implemented using Sikuli to simulate user interactions with screen elements.
- OCR for Document & Screen Extraction: Integrated Tesseract OCR to extract structured data from identity documents.
- Preprocessing for Accuracy: Used OpenCV to crop, deskew, and enhance scanned document images before processing.
- Cross-Platform Execution Support: Enabled headless automation using XVFB for Linux and for Windows environments.
- Modular Codebase in Java: Built with Spring Framework, offering high maintainability.
- Data Management: Utilized MongoDB to store and retrieve OCR results in a structured, scalable format.
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