Abstract
This paper introduces a comprehensive end-to-end pipeline for Optical Character Recognition (OCR) on Urdu newspapers. In our approach, we address the unique challenges of complex multi-column layouts, low-resolution archival scans, and diverse font styles. Our process decomposes the OCR task into four key modules: (1) article segmentation, (2) image super-resolution, (3) column segmentation, and (4) text recognition. For article segmentation, we fine-tune and evaluate YOLOv11x to identify and separate individual articles from cluttered layouts. Our model achieves a precision of 0.963 and mAP@50 of 0.975. For super-resolution, we fine-tune and benchmark the SwinIR model (reaching 32.71 dB PSNR) to enhance the quality of degraded newspaper scans. To do our column segmentation, we use YOLOv11x to separate columns in text to further enhance performance - this model reaches a precision of 0.970 and mAP@50 of 0.975. In the text recognition stage, we benchmark a range of LLMs from different families, including Gemini, GPT, Llama, and Claude. The lowest WER of 0.133 is achieved by Gemini-2.5-Pro.
Abstract (translated)
本文介绍了一种针对乌尔都语报纸的光学字符识别（OCR）的端到端综合管道。我们的方法解决了复杂多栏布局、低分辨率档案扫描和多样化字体风格的独特挑战。该过程将OCR任务分解为四个关键模块：(1) 文章分割，(2) 图像超分辨率，(3) 栏分割，以及 (4) 文本识别。 对于文章分割，我们微调并评估了YOLOv11x模型，以从复杂布局中识别和分离出单独的文章。我们的模型达到了0.963的精确度（precision）和0.975的mAP@50指标。在超分辨率阶段，我们微调并基准测试了SwinIR模型，使其达到32.71 dB PSNR，以提高受损报纸扫描的质量。对于栏分割，我们使用YOLOv11x将文本中的各栏目分离出来，进一步提升性能——该模型达到了0.970的精确度和0.975的mAP@50指标。 在文本识别阶段，我们测试了一系列来自不同家族的大规模语言模型（LLM），包括Gemini、GPT、Llama 和 Claude。最低词错误率（WER）为 0.133 的是由 Gemini-2.5-Pro 模型实现的。
URL
https://arxiv.org/abs/2505.13943
