scikit-learn-contrib/imbalanced-learn
quality grade B, 72 out of 100A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
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ETL, document parsing, web crawling, synthetic data generation, annotation and dataset curation tooling.
Signals: data-engineering, etl, data-pipeline, web-scraping, crawler, data-labeling, annotation-tool, synthetic-data
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A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
汉字字符特征提取器 (featurizer),提取汉字的特征(发音特征、字形特征)用做深度学习的特征 | A Chinese character feature extractor, which extracts the features of Chinese characters (pronunciation features, glyph features) as features for deep learning
This is a reading list for deep learning for OCR
An efficient video loader for deep learning with smart shuffling that's super easy to digest
Generate text line images for training deep learning OCR models
Deep Learning Pipelines for Apache Spark
This library augments road images to introduce various real world scenarios that pose challenges for training neural networks of Autonomous vehicles. Automold is created to train CNNs in specific weather and road conditions.
Deep neural network to extract intelligent information from invoice documents.
Neural network OCR.
huggingface mirror download
利用HuggingFace的官方下载工具从镜像网站进行高速下载。
A python program that turns an LLM, running on Ollama, into an automated researcher, which will with a single query determine focus areas to investigate, do websearches and scrape content from various relevant websites and do research for you all on its own! And more, not limited to but including saving the findings for you!
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🔥 Visual workflow builder for AI agents powered by Firecrawl - drag-and-drop web scraping pipelines with real-time execution
Knowledge Agents and Management in the Cloud
YEDDA: A Lightweight Collaborative Text Span Annotation Tool. Code for ACL 2018 Best Demo Paper Nomination.
Open source annotation tool for machine learning practitioners.
An app to find text in real life.
这是我自己做的一个类似滴滴打车的Android出行项目,主要针对滴滴等出行平台一直饱受质疑的“人车不符”问题,以及当前越发火热的或计划和出海战略,给出行项目增加了下面几个功能: 1. RFID识别验证功能:在司机证件或者车内识别硬件嵌入RFID识别芯片,乘客使用手机读取到芯片信息,并且通过网络发送到出行平台数据库进行验证(我用JNI加了一个C语言的MD5加密算法对识别到的信息进行了加密)。如果不是合规的“人”或“车”,则不能完成订单并向平台或监管单位汇报当前位置。(为了方便读者测试,可以使用手机读取任何一个加密或非加密RFID芯片,比如银行卡、公交卡等,我在代码中的验证前阶段把芯片信息都换成我自己的司机信息,确保读者测试时可以收到服务器的回复) 2. 海外版功能:点击切换当前语言。 3. 司机证件号码识别功能:读取司机证件上的证件号码,也可以用来与出行平台数据库的接单司机信息进行。I complete this whole project on my own . Including Android application programming,web server ( Apache + PHP + MySQL), and UI. 1.Map route planing。You can use mobile phone choose pick up & destination address,application provide address name hint and draw optimized route for user , then call car for you. 2.RFID authentication function:User can use application to scan driver license or other RFID hardware, then use NDK MD5 algorithm encrypt RFID number, and send it to Web Server Database, check with driver information and authenticate ID number , if ID number coherent with driver info , send back driver information to User and continue call car order . record user location and alert if ID not coherent. 3.Driver License Number Recognition : Application can recognize driver license digit number ,and also can send to web server for authentication & feed back result to user.
Open Source research tool to search, browse, analyze and explore large document collections by Semantic Search Engine and Open Source Text Mining & Text Analytics platform (Integrates ETL for document processing, OCR for images & PDF, named entity recognition for persons, organizations & locations, metadata management by thesaurus & ontologies, search user interface & search apps for fulltext search, faceted search & knowledge graph)
Detecting Text in Natural Image with Connectionist Text Proposal Network (ECCV'16)
主流ocr算法研究实验性的项目,目前实现了CNN+BLSTM+CTC架构
: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).
How to Copy Text from Images ? Answer is TextSnatcher !. Perform OCR operations in seconds on Linux Desktop.
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