Papers with Code 아카이브
Table Recognition
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Table recognition refers to the process of automatically identifying and extracting tabular structures from unstructured data sources such as text documents, images, or scanned documents. The goal of table recognition is to accurately detect the presence of tables within the data and extract their contents, including rows, columns, headers, and cell values.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JG1VPP/MuTabNet
공식 코드Multi-Cell Decoder and Mutual Learning for Table Structure and Character Recognition
https://github.com/PaddlePaddle/PaddleOCR
공식 코드https://github.com/PaddlePaddle/VIMER/tree/main/StrucTexT/v2
공식 코드StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training
https://github.com/Psarpei/Multi-Type-TD-TSR
공식 코드https://github.com/abdoelsayed2016/table-detection-structure-recognition
공식 코드Deep learning for table detection and structure recognition: A survey
https://github.com/alibabaresearch/advancedliteratemachinery
공식 코드LORE: Logical Location Regression Network for Table Structure Recognition
https://github.com/cycloneboy/pdf_table
공식 코드PdfTable: A Unified Toolkit for Deep Learning-Based Table Extraction
https://github.com/hikopensource/davar-lab-ocr
공식 코드LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment
https://github.com/hpi-information-systems/mondrian
공식 코드https://github.com/iWudao/Synthesizing-Realistic-Data-for-Table-Recognition
공식 코드https://github.com/ibm-aur-nlp/PubLayNet
공식 코드https://github.com/ibm-aur-nlp/PubTabNet
공식 코드https://github.com/lqzxt/ngtr
공식 코드Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner
https://github.com/maxkinny/tabrecset
공식 코드A large-scale dataset for end-to-end table recognition in the wild
https://github.com/microsoft/table-transformer
공식 코드PubTables-1M: Towards comprehensive table extraction from unstructured documents
https://github.com/mnamysl/table-interpretation
공식 코드Flexible Table Recognition and Semantic Interpretation System
https://github.com/namtuanly/MTL-TabNet
공식 코드An End-to-End Multi-Task Learning Model for Image-based Table Recognition
https://github.com/namtuanly/wikitableset
공식 코드Rethinking Image-based Table Recognition Using Weakly Supervised Methods
https://github.com/poloclub/tsr-convstem
공식 코드High-Performance Transformers for Table Structure Recognition Need Early Convolutions
https://github.com/poloclub/unitable
공식 코드UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretraining
https://github.com/sachinraja13/TabStructNet
공식 코드Table Structure Recognition using Top-Down and Bottom-Up Cues
https://github.com/shahrukhqasim/TIES-2.0
공식 코드https://github.com/wenwenyu/MASTER-pytorch
공식 코드https://github.com/xuewenyuan/TGRNet
공식 코드TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition
https://github.com/JiaquanYe/TableMASTER-mmocr
https://github.com/Line290/EDD-third-party
https://github.com/abdoelsayed2016/TNCR_Dataset
Deep learning for table detection and structure recognition: A survey
https://github.com/abdoelsayed2016/tncr
Deep learning for table detection and structure recognition: A survey
https://github.com/hassan-mahmood/TIES_DataGeneration
https://github.com/hmnth1/table_ocr
https://github.com/hnmspirit/table_datagen
https://github.com/kewin1807/table-recognition
https://github.com/phamquiluan/table-transformer
PubTables-1M: Towards comprehensive table extraction from unstructured documents
https://github.com/sohaib023/gnn-table-recognition
https://github.com/virtualsociety/ai-table-recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.