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
Samved Divekar enhanced the docling-project/docling-eval repository by building and refining cross-provider document data extraction features using Python. He implemented layout-aware extraction and SegmentedPage support for AWS Textract and Azure Document Intelligence, enabling richer, structured outputs and robust table parsing. His work included integrating Google Document AI for word-level OCR, expanding test coverage, and addressing parsing issues to improve reliability. In June, he focused on cloud table processing, resolving text duplication and runtime errors across Azure and Google integrations. Divekar’s contributions demonstrated depth in API integration, error handling, and cloud services, resulting in more reliable, maintainable document processing pipelines.
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 people
Work 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.
Activity
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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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