MONSTERMASH: Multidirectional, Overlapping, Nested, Spiral Text Extraction for Recognition Models of Arabic-Script Handwriting

Most current models for handwritten text recognition transcribe individual lines and thus depend on accurate line extraction from page images. This line extraction task is particularly challenging for Arabic-script manuscripts, which exhibit a high proportion of curved lines, word baselines that vary within the line, and varying line orientation on the page. We present a new corpus for studying Arabic-script line extraction in the presence of these phenomena and evaluate different model architectures using several pixel-level, object-level, and extrinsic recognition metrics. Training all models on the same data, we find that the CNN-based Kraken model slightly outperforms the transformer-based TESTR model on recognition character accuracy and some object-level metrics, even though it lags behind on pixel-level metrics.