From Press to Pixels: Evolving Urdu Text Recognition

• URL: http://arxiv.org/abs/2505.13943v2
• Date: Fri, 01 Aug 2025 15:56:30 GMT
• Title: From Press to Pixels: Evolving Urdu Text Recognition
• Authors: Samee Arif, Sualeha Farid,
• Abstract summary: This paper introduces an end-to-end pipeline for Optical Character Recognition (OCR) on Urdu newspapers.<n>It addresses challenges posed by complex multi-column layouts, low-resolution scans, and the stylistic variability of the Nastaliq script.<n>We fine-tune YOLOv11x for segmentation, achieving 0.963 precision for articles and 0.970 for columns.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces an end-to-end pipeline for Optical Character Recognition (OCR) on Urdu newspapers, addressing challenges posed by complex multi-column layouts, low-resolution scans, and the stylistic variability of the Nastaliq script. Our system comprises four modules: (1) article segmentation, (2) image super-resolution, (3) column segmentation, and (4) text recognition. We fine-tune YOLOv11x for segmentation, achieving 0.963 precision for articles and 0.970 for columns. A SwinIR-based super-resolution model boosts LLM text recognition accuracy by 25-70%. We also introduce the Urdu Newspaper Benchmark (UNB), a manually annotated dataset for Urdu OCR. Using UNB and the OpenITI corpus, we compare traditional CNN+RNN-based OCR models with modern LLMs. Gemini-2.5-Pro achieves the best performance with a WER of 0.133. We further analyze LLM outputs via insertion, deletion, and substitution error breakdowns, as well as character-level confusion analysis. Finally, we show that fine-tuning on just 500 samples yields a 6.13% WER improvement, highlighting the adaptability of LLMs for Urdu OCR.

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