Abstract
Optical Character Recognition (OCR) in multilingual, noisy, and diverse real-world images remains a significant challenge for optical character recognition systems. With the rise of Large Vision-Language Models (LVLMs), there is growing interest in their ability to generalize and reason beyond fixed OCR pipelines. In this work, we introduce Sprinklr-Edge-OCR, a novel OCR system built specifically optimized for edge deployment in resource-constrained environments. We present a large-scale comparative evaluation of five state-of-the-art LVLMs (InternVL, Qwen, GOT OCR, LLaMA, MiniCPM) and two traditional OCR systems (Sprinklr-Edge-OCR, SuryaOCR) on a proprietary, doubly hand annotated dataset of multilingual (54 languages) images. Our benchmark covers a broad range of metrics including accuracy, semantic consistency, language coverage, computational efficiency (latency, memory, GPU usage), and deployment cost. To better reflect real-world applicability, we also conducted edge case deployment analysis, evaluating model performance on CPU only environments. Among the results, Qwen achieved the highest precision (0.54), while Sprinklr-Edge-OCR delivered the best overall F1 score (0.46) and outperformed others in efficiency, processing images 35 faster (0.17 seconds per image on average) and at less than 0.01 of the cost (0.006 USD per 1,000 images) compared to LVLM. Our findings demonstrate that the most optimal OCR systems for edge deployment are the traditional ones even in the era of LLMs due to their low compute requirements, low latency, and very high affordability.
Abstract (translated)
光学字符识别（OCR）在多语言、嘈杂且多样化的现实世界图像中仍然是一项重大挑战。随着大型视觉-语言模型（LVLM）的兴起，人们越来越关注这些模型超越固定OCR管道的能力。在此项研究中，我们介绍了一种名为Sprinklr-Edge-OCR的新颖OCR系统，该系统专门为资源受限环境中的边缘部署进行了优化。 我们在一个多语言（54种语言）、双人标注的专有数据集上对五种最先进的LVLM模型（InternVL、Qwen、GOT OCR、LLaMA和MiniCPM）以及两种传统OCR系统（Sprinklr-Edge-OCR和SuryaOCR）进行了大规模对比评估。我们的基准测试涵盖了包括准确性、语义一致性、语言覆盖率、计算效率（延迟、内存、GPU使用率）和部署成本在内的广泛指标。 为了更好地反映现实世界的适用性，我们在仅CPU环境的边缘案例部署中也进行了模型性能评估。研究结果显示，在精度方面，Qwen表现最佳（0.54），而Sprinklr-Edge-OCR在总体F1分数（0.46）上取得最优，并且在效率、处理图像速度快35倍（平均每张图片耗时0.17秒）以及成本低廉（每1,000张图片不到0.01美元，即0.006美元）方面优于其他模型。 我们的研究发现表明，在大型语言模型的时代，最适合边缘部署的OCR系统依然是传统的那些，这是由于它们对计算资源需求低、延迟短且极其经济实惠。
URL
https://arxiv.org/abs/2509.03615
