Developed and delivered a handwritten text recognition feature for the DS4SD/docling-core repository, enabling automated extraction of handwritten content from documents. The work involved integrating HANDWRITTEN_TEXT label support into core modules such as tokens.py and DEFAULT_EXPORT_LABELS, along with establishing a complete end-to-end recognition flow. Comprehensive integration tests were implemented in Python to ensure reliability and correctness, with collaborative development and DCO-compliant commits. This feature streamlines data capture from handwritten sources, reducing manual transcription effort and supporting downstream analytics. The project demonstrated full stack development and testing skills, with a focus on automation, cross-team collaboration, and robust code integration.