Based on our record, OpenCV should be more popular than DocParser. It has been mentiond 50 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 5 months ago
You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/). Source: 5 months ago
- [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection. Source: 9 months ago
I came across a very interesting [project]( (4) Mckay Wrigley on Twitter: "My goal is to (hopefully!) add my house to the dataset over time so that I have an indoor assistant with knowledge of my surroundings. It’s basically just a slow process of building a good enough dataset. I hacked this together for 2 reasons: 1) It was fun, and I wanted to…" / X ) made by Mckay Wrigley and I was wondering what's the easiest... Source: 9 months ago
You also need C++ if you're going to do things which aren't built in as part of the engine. As an example if you're looking at using compute shaders, inbuilt native APIs such as a mobile phone's location services, or a third-party library such as OpenCV, then you're going to need C++. Source: 11 months ago
You could try an online service like https://extract-io.web.app/ or https://docparser.com/. Source: 11 months ago
DocParser: DocParser simplifies the extraction of structured data from various file formats, such as PDFs and scanned documents, directly into Google Sheets. By automating this process, DocParser saves valuable time and effort otherwise spent on manual data entry. Link to DocParser. Source: 12 months ago
There are several tools available today that can help you extract tables from PDF files (such as Tabula), or even parse PDFs into structured JSON using AI (like Parsio -> I'm the founder) or without AI (like Docparser). Source: about 1 year ago
Thank you for sharing those! I didn't know them I've only checked this one https://docparser.com/ and I think my solution could be better because it will be easier for the user. Source: about 1 year ago
As previously suggested, if the layout of your PDFs never changes (consistent column widths in tables and placement), you can use a zonal PDF parser like DocParser. Alternatively, an AI-powered parser may be a better choice. Source: about 1 year ago
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
FlexiCapture - ABBYY FlexiCapture brings together the best NLP, machine learning, and advanced recognition capabilities into a single, enterprise-scale platform to handle every type of document. Available in the Cloud, on premise or as SDK.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Amazon Textract - Easily extract text and data from virtually any document using Amazon Textract. Textract goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables.
NumPy - NumPy is the fundamental package for scientific computing with Python
Docsumo - Extract Data from Unstructured Documents - Easily. Efficiently. Accurately.