On Cropped versus Uncropped Training Sets in Tabular Structure Detection
Automated document processing for tabular information extraction is highly desired in many organizations, from industry to government. Prior works have addressed this problem under table detection and table structure detection tasks. Proposed solutions leveraging deep learning approaches have been giving promising results in these tasks. However, the impact of dataset structures on table structure detection has not been investigated. In this study, we provide a comparison of table structure detection performance with cropped and uncropped datasets. The cropped set consists of only table images that are cropped from documents assuming tables are detected perfectly. The uncropped set consists of regular document images. Experiments show that deep learning models can improve the detection performance by up to 9% in average precision and average recall on the cropped versions. Furthermore, the impact of cropped images is negligible under the Intersection over Union (IoU) values of 50%-70% when compared to the uncropped versions. However, beyond 70% IoU thresholds, cropped datasets provide significantly higher detection performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Table DetectionSimilar Papers 제목 키워드 기반
A Simple Transformer Pipeline for Full-Key Side-Channel Attacks on Uncropped Datasets
Deep learning-based side-channel analysis has historically focused on single-byte targets and manually cropped traces, which risks discarding exploitable leakage. While recent work has proposed specialized architectures …
Learning Subject-Aware Cropping by Outpainting Professional Photos
How to frame (or crop) a photo often depends on the image subject and its context; e.g., a human portrait. Recent works have defined the subject-aware image cropping task as a nuanced and practical version of image cropp…
Image CroppingBrain Tumor Classification Using Deep Learning Technique -- A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes
Deep Learning is the newest and the current trend of the machine learning field that paid a lot of the researchers' attention in the recent few years. As a proven powerful machine learning tool, deep learning was widely …
BIG-bench Machine LearningBrain Tumor ClassificationDeep LearningSensitivityUsing CNNs to Identify the Origin of Finger Vein Image
We study the finger vein (FV) sensor model identification task using a deep learning approach. So far, for this biometric modality, only correlation-based PRNU and texture descriptor-based methods have been applied. We e…
Faceness-Net: Face Detection through Deep Facial Part Responses
We propose a deep convolutional neural network (CNN) for face detection leveraging on facial attributes based supervision. We observe a phenomenon that part detectors emerge within CNN trained to classify attributes from…
Face Detection