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PDF Table Extractor is an innovative Python project designed to tackle the challenge of extracting tables from scanned PDF documents. Leveraging advanced optical character recognition (OCR) and image processing techniques.
Table Structure Recognition package containing server-client application with a trained neural network for detecting tables and recognizing their structure
Add the Grid Search functionality to search for optimal hyperparameters while fine-tuning the model. Table Transformer (TATR) is a deep learning model for extracting tables from unstructured documents (PDFs and images).
智能文本自动处理工具(Intelligent text automatic processing tool)。AutoText的功能主要有文本纠错,图片ocr、版面检测以及表格结构识别等。The main functions of this project include text error correction, ocr, layout-detection and table structure recognition.
Table Transformer (TATR) is a deep learning model for extracting tables from unstructured documents (PDFs and images). This is also the official repository for the PubTables-1M dataset and GriTS evaluation metric.
This repository contains the code and implementation details of the CascadeTabNet paper "CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents"