docling-project/docling-eval
May 2025 – Jun 2025
2 Months active
Languages Used
Python
Technical Skills
API IntegrationAWS TextractAzure AI Document IntelligenceBackend DevelopmentBug FixingCloud Services
Samved Divekar
PROFILE
Samved Divekar
Worked on the docling-project/docling-eval repository to enhance layout-aware data extraction and cross-cloud document processing. Focused on integrating AWS Textract, Azure Document Intelligence, and Google Document AI, the developer implemented segmented page support, word-level OCR, and robust table extraction pipelines using Python. Expanded test coverage across multiple datasets to ensure reliability and prevent regressions, while addressing issues such as text duplication, overlapping content, and error handling in cloud-based workflows. Improvements to provenance data management and prediction provider stability resulted in higher data quality and smoother analytics for downstream consumers, demonstrating strong skills in API integration, backend development, and cloud services.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
6Total
Bugs
2
Commits
6
Features
3
Lines of code
2,498
Activity Months2
Your Network
6 peopleWork History
June 2025
1 Commits
Jun 1, 2025June 2025: Delivered targeted reliability improvements in the docling-eval cloud table processing module. Fixed text duplication in table extraction across Azure and Google, refined how table and paragraph data are extracted to prevent overlapping content, and improved handling of provenance items. Also resolved a divide-by-zero error in Google's prediction provider, stabilizing predictions for cloud-based workloads. These changes reduce data quality issues, prevent runtime errors, and enhance cross-cloud compatibility for downstream analytics and evaluation pipelines.
1 Commits
Jun 1, 2025June 2025: Delivered targeted reliability improvements in the docling-eval cloud table processing module. Fixed text duplication in table extraction across Azure and Google, refined how table and paragraph data are extracted to prevent overlapping content, and improved handling of provenance items. Also resolved a divide-by-zero error in Google's prediction provider, stabilizing predictions for cloud-based workloads. These changes reduce data quality issues, prevent runtime errors, and enhance cross-cloud compatibility for downstream analytics and evaluation pipelines.
June 2025
May 2025
5 Commits • 3 Features
May 1, 2025May 2025 performance summary for docling-eval: Delivered cross-provider layout-aware data extraction enhancements and strengthened reliability across AWS Textract, Azure Document Intelligence, and Google Document AI integrations. Key improvements include layout extraction, SegmentedPage support, and word-level OCR, backed by expanded test coverage. These efforts deliver richer, layout-aware predictions, improved data extraction robustness, and higher downstream value for customers relying on Docling's structured outputs.
May 2025
5 Commits • 3 Features
May 1, 2025May 2025 performance summary for docling-eval: Delivered cross-provider layout-aware data extraction enhancements and strengthened reliability across AWS Textract, Azure Document Intelligence, and Google Document AI integrations. Key improvements include layout extraction, SegmentedPage support, and word-level OCR, backed by expanded test coverage. These efforts deliver richer, layout-aware predictions, improved data extraction robustness, and higher downstream value for customers relying on Docling's structured outputs.
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture81.6%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API IntegrationAWS TextractAzure AI Document IntelligenceBackend DevelopmentBug FixingCloud ServicesCloud Services IntegrationData ExtractionData ModelingData ParsingDocument AIDocument AnalysisDocument ProcessingError HandlingIntegration Testing
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