Hybrid semantic-LLM method for reading order reconstruction in Armenian historical newspapers outperforms baselines on a new 66-page dataset while releasing a specialized Tesseract OCR model.
Multi-Task Handwritten Document Layout Analysis
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abstract
Document Layout Analysis is a fundamental step in Handwritten Text Processing systems, from the extraction of the text lines to the type of zone it belongs to. We present a system based on artificial neural networks which is able to determine not only the baselines of text lines present in the document, but also performs geometric and logic layout analysis of the document. Experiments in three different datasets demonstrate the potential of the method and show competitive results with respect to state-of-the-art methods.
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BADAM is a public dataset of 400 annotated Arabic manuscript images paired with a fully convolutional network for baseline detection and text line extraction.
Survey proposing a taxonomy for document parsing into pipeline-based systems and VLM-driven unified models, reviewing components, metrics, benchmarks, and challenges.
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BADAM: A Public Dataset for Baseline Detection in Arabic-script Manuscripts
BADAM is a public dataset of 400 annotated Arabic manuscript images paired with a fully convolutional network for baseline detection and text line extraction.