OCR-Form-Tools | open source labeling tool for Form Recognizer | Data Labeling library

by huangjinbao TypeScript Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | OCR-Form-Tools Summary

OCR-Form-Tools is a TypeScript library typically used in Artificial Intelligence, Data Labeling applications. OCR-Form-Tools has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

An open source labeling tool for Form Recognizer, part of the Form OCR Test Toolset (FOTT). The purpose of this repo is to allow customers to test various tools when working with Microsoft Forms and OCR services. Currently, Labeling tool is the first tool we present here. Users could provide feedback, and make customer-specific changes to meet their unique needs. Microsoft Azure Form Recognizer team will update the source code periodically. If you would like to contribute, please check the contributing section. FOTT's Labeling Tool is a React + Redux Web application, written in TypeScript. This project was bootstrapped with Create React App. Current Features of Labeling Tool: (you can view a short demo here).

Support

Quality

Security

License

Reuse

Support

OCR-Form-Tools has a low active ecosystem.

It has 2 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

OCR-Form-Tools has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of OCR-Form-Tools is current.

Quality

OCR-Form-Tools has no bugs reported.

Security

OCR-Form-Tools has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

OCR-Form-Tools is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

OCR-Form-Tools releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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OCR-Form-Tools Key Features

No Key Features are available at this moment for OCR-Form-Tools.

OCR-Form-Tools Examples and Code Snippets

No Code Snippets are available at this moment for OCR-Form-Tools.

Community Discussions

Trending Discussions on Data Labeling

How can I do this split process in Python?

Replacing a character with a space and dividing the string into two words in R

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

QUESTION

How can I do this split process in Python?

Asked 2021-Dec-30 at 14:06

I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

What I've done so far is make this representation with the same enumerator class.

A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

...

ANSWER

Answered 2021-Dec-30 at 13:57

Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

Source https://stackoverflow.com/questions/70532286

QUESTION

Replacing a character with a space and dividing the string into two words in R

Asked 2020-Nov-18 at 07:32

I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

i have this:

...

ANSWER

Answered 2020-Nov-16 at 07:24

Try something like:

Source https://stackoverflow.com/questions/64853962

QUESTION

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

Asked 2020-Oct-28 at 20:31

Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.

...

ANSWER

Answered 2020-Oct-27 at 22:39

Is the data behind virtual network by any chance?

Source https://stackoverflow.com/questions/64546521

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install OCR-Form-Tools

Form Labeling Tool requires NodeJS (>= 10.x, Dubnium) and NPM.
Please see instructions here.
To go thru a complete label-train-analyze scenario, you need a set of at least six forms of the same type. You will label five forms to train a model and one form to test the model. You could upload the sample files to the root of a blob storage container in an Azure Storage account. For advanced scenarios where there are forms in quite different formats, you could organize them into subfolders based on similar format. When you set up your project to train a model from one format, you need to specify a subfolder in the project setting page. Enable CORS on your storage account. Select your storage account in the Azure portal and click the CORS tab on the left pane. On the bottom line, fill in the following values. Then click Save at the top.
Allowed origins = *
Allowed methods = [select all]
Allowed headers = *
Exposed headers = *
Max age = 200

Support

There are many ways to contribute to OCR Form Labeling Tool -- please review our contribution guidelines. This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

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