A Repo For Document AI

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"

整理目前开源的最优表格识别模型，完善前后处理，模型转换为ONNX Organize the currently open-source optimal table recognition models, improve pre-processing and post-processing, and convert the models to ONNX.

A curated list of resources dedicated to table recognition

ACM Multimedia 2023: DocDiff: Document Enhancement via Residual Diffusion Models. Also contains 1597 red seals in Chinese scenes, along with their corresponding binary masks.

A toolbox of ocr models and algorithms based on MindSpore

文档方向分类

Dedoc is a library (service) for automate documents parsing and bringing to a uniform format. It automatically extracts content, logical structure, tables, and meta information from textual electronic documents. (Parse document; Document content extraction; Logical structure extraction; PDF parser; Scanned document parser; DOCX parser; HTML parser

Table Detection and Extraction Using Deep Learning ( It is built in Python, using Luminoth, TensorFlow<2.0 and Sonnet.)

基于序列表格识别算法推理库，集成PP-Structure和modelscope等表格识别算法。

Deep learning, Convolutional neural networks, Image processing, Document processing, Table detection, Page object detection, Table classification. https://www.sciencedirect.com/science/article/pii/S0925231221018142

Table Structure Recognition

Complex data extraction and orchestration framework designed for processing unstructured documents. It integrates AI-powered document pipelines (GenAI, LLM, VLLM) into your applications, supporting various tasks such as document cleanup, optical character recognition (OCR), classification, splitting, named entity recognition, and form processing

Google Colab Demo of CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents

Extracting Tabular Data from Image to Excel files

A Unified Toolkit for Deep Learning-Based Table Extraction

code for participation in ICDAR2021 Table Recognition track (Team Name: LTIAYN = Kaen Context)

Compute benchmark of table structure recognition.

Table Detection from the Given Pictures or Files

利用Swin-Unet(Swin Transformer Unet)实现对文档图片里表格结构的识别，Swin-unet (Swin Transformer Unet) is used to identify the document table structure