Urdu Katib Handwritten Dataset: A Historical Document Dataset for Offline Urdu Handwritten Text Recognition with CRNN-Based Baseline Evaluation
arXiv:2606.19139v1 Announce Type: cross Abstract: Automatic Handwritten Text Recognition (HTR) is inherently a challenging task, and its complexity is further increased when dealing with cursive scripts. Although significant efforts have been made on various cursive scripts, research regarding Urdu Handwritten Text Recognition (UHTR) has been relatively limited. This lag of research is primarily due to the unique challenges posed by its script, and the scarcity and unavailability of benchmark datasets. Therefore, to advance research in UHTR, this study presents a specialized real dataset calle
The continuous drive for more inclusive and robust AI, particularly in less-resourced languages, is spurring new dataset creation to overcome current technological limitations.
This development addresses a critical data scarcity issue in Urdu handwritten text recognition, which can unlock access to historical documents and improve AI applications for a significant language population.
The availability of a specialized benchmark dataset for Urdu handwritten text recognition enhances research and development in Natural Language Processing for non-English, cursive scripts.
- · Urdu language speakers
- · NLP researchers
- · Cultural heritage preservation initiatives
- · AI developers in South Asia
- · Monolingual OCR systems
- · Researchers reliant solely on Western-centric datasets
Improved accuracy and broader adoption of Urdu handwritten text recognition in various applications.
Potential for new AI applications for historical document analysis, education, and digital archiving in Urdu-speaking regions.
Increased digital accessibility and preservation of Urdu literary and historical heritage, potentially fostering local AI ecosystems.
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