Abstract
Object Recognition and Document Skew Estimation have come a long way in terms of performance and efficiency. New models follow one of two directions: improving performance using larger models, and improving efficiency using smaller models. However, real-time document detection and rectification is a niche that is largely unexplored by the literature, yet it remains a vital step for automatic information retrieval from visual documents. In this work, we strive towards an efficient document detection pipeline that is satisfactory in terms of Optical Character Recognition (OCR) retrieval and faster than other available solutions. We adapt IWPOD-Net, a license plate detection network, and train it for detection on NBID, a synthetic ID card dataset. We experiment with data augmentation and cross-dataset validation with MIDV (another synthetic ID and passport document dataset) to find the optimal scenario for the model. Other methods from both the Object Recognition and Skew Estimation state-of-the-art are evaluated for comparison with our approach. We use each method to detect and rectify the document, which is then read by an OCR system. The OCR output is then evaluated using a novel OCR quality metric based on the Levenshtein distance. Since the end goal is to improve automatic information retrieval, we use the overall OCR quality as a performance metric. We observe that with a promising model, document rectification does not have to be perfect to attain state-of-the-art performance scores. We show that our model is smaller and more efficient than current state-of-the-art solutions while retaining a competitive OCR quality metric. All code is available at this https URL
Abstract (translated)
物体识别和文档倾斜估计在性能和效率方面已经有了显著的进步。新的模型主要沿着两个方向发展：一是通过使用更大的模型来提高性能，二是通过采用更小的模型来提升效率。然而，实时文档检测与校正是一个文献中尚未充分探索的小众领域，但它对于从视觉文件自动检索信息来说至关重要。在这项工作中，我们致力于开发一种高效的文档检测流水线，在光学字符识别（OCR）检索方面达到令人满意的效果，并且速度要快于现有的解决方案。 我们将IWPOD-Net（一种车牌检测网络），并对其进行了训练以在NBID（一个合成的身份证卡数据集）上进行检测任务。我们还通过使用MIDV（另一个包含合成身份证和护照文件的数据集）来进行数据增强和跨数据集验证，以此来寻找模型的最佳应用情景。我们也评估了来自物体识别和倾斜估计领域的其他顶级方法，并将其与我们的方法进行了比较。 每个方法被用来检测并校正文档，然后通过OCR系统进行读取。之后，我们使用基于Levenshtein距离的新OCR质量指标对输出结果进行评价。由于最终目标是提高自动信息检索的效率，我们将整体OCR的质量作为性能指标来衡量。我们发现即使在模型具备一定潜力的情况下，文档矫正并不需要达到完美程度就能取得顶尖的表现分数。 我们的实验结果显示，相较于当前的顶级解决方案，我们的模型不仅更小、更具效率，并且保持了相当竞争力的OCR质量指标。所有代码可在提供的网址获取。
URL
https://arxiv.org/abs/2509.06246
