Extraction of Handwritten and Printed Cyrillic Text from Documents: A Resource-Efficient Pipeline

Abstract

The digitisation of historical, administrative, and personal documents in Bulgarian faces considerable challenges due to the lack of robust Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) systems tailored for the Cyrillic alphabet. While modern Vision-Language Models (VLMs) and large transformer-based architectures achieve state-ofthe-art results, their performance and resource efficiency on low-resource languages remain prohibitive for decentralised, privacy-preserving applications. In this paper, we present a comprehensive, resource-efficient pipeline for extracting printed and handwritten Cyrillic text. Our system integrates advanced image preprocessing, YOLO-based document structure and table recognition, and a Permuted Autoregressive Sequence (PARSeq) model trained specifically for Bulgarian. We generated custom synthetic Bulgarian cursive datasets to mitigate the severe lack of real-world training data. Our evaluation indicates that the specialised PARSeq model outperforms traditional OCR tools such as Tesseract and EasyOCR on our custom degraded printed test set, and provides a practical, resource-efficient baseline for handwriting recognition compared to a modern local VLM (Qwen3-VL-4B). Finally, we discuss the discrepancy between synthetic and real handwritten data, highlighting the urgent need for a standardised, annotated Bulgarian HTR dataset.