ZeroCodePro's repositories
AdelaiDet
AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
calamari
Line based ATR Engine based on OCRopy
Kosmos2.5
My implementation of Kosmos2.5 from the paper: "KOSMOS-2.5: A Multimodal Literate Model"
barcode-datasets
A list of available Barcode & QR Code Datasets
table-transformer
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.
unilm
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
OpenNMT-py
Open Source Neural Machine Translation in PyTorch
U2Net-Multi-Gpus-Training
U-2-Net mutli-gpus training Pytorch Lightning code
FormulaNet
FormulaNet is a new large-scale Mathematical Formula Detection dataset.
LaTeX-OCR
pix2tex: Using a ViT to convert images of equations into LaTeX code.
CoMER
Official implementation for ECCV 2022 paper "CoMER: Modeling Coverage for Transformer-based Handwritten Mathematical Expression Recognition"
math-formula-recognition
Math formula recognition (Images to LaTeX strings)
im2markup
Neural model for converting Image-to-Markup (by Yuntian Deng yuntiandeng.com)
LaTeX_OCR_PRO
:art: 数学公式识别增强版：中英文手写印刷公式、支持初级符号推导（数据结构基于 LaTeX 抽象语法树）Math Formula OCR Pro, supports handwrite, Chinese-mixed formulas and simple symbol reasoning (based on LaTeX AST).
mmdetection
OpenMMLab Detection Toolbox and Benchmark
TableMASTER-mmocr
2nd solution of ICDAR 2021 Competition on Scientific Literature Parsing, Task B.
Formula2LaTeX
Web app and telegram bot for formula recognition from photos
TableNet
Unofficial implementation of "TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images"
ThinkMatch
Code & pretrained models of novel deep graph matching methods.
pdfplumber
Plumb a PDF for detailed information about each char, rectangle, line, et cetera — and easily extract text and tables.
ICDAR2019_cTDaR
The ICDAR 2019 cTDaR is to evaluate the performance of methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables are provided. For TRACK B two subtracks exist: the first subtrack (B.1) provides the table region. Thus, only the table structure recognition must be performed. The second subtrack (B.2) provides no a-priori information. This means, the table region and table structure detection has to be done.
Multi-Type-TD-TSR
Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition:
TGRNet
TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition
ICDAR2021_MFD
1st Solution For ICDAR 2021 Competition on Mathematical Formula Detection（公式检测冠军方案）
SPLERGE
Deep Splitting and Merging for Table Structure Decomposition
CDLA
CDLA: A Chinese document layout analysis (CDLA) dataset
CascadeTabNet
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"