Kroki provides an HTTP API to create diagrams from textual descriptions. Kroki handles both GET and POST requests. When using GET requests, your diagram must be encoded in the URL using a deflate + base64 algorithm. But don't worry, if you're not familiar with deflate or base64 (or if you don't want to use them), you can also send your diagram as plain text using POST requests (see below).
Free Text to Diagram Converter API to Convert Text Diagrams to Images
It's also possible to send your diagram as plain text using the Content-Type header. The output format will be specified using the Accept header and the diagram source will be sent as the request body:
Kroki is a free text to diagram converter API to convert text diagrams to images. With this API, you make requests to an endpoint with text diagram data and it returns an image. The image can be PNG or SVG and then you can use that anywhere you like. This service supports some popular diagram tools such as GraphViz, Mermaid, PlantUML, SvgBob, UMLet, and some others. There are different endpoints available where you can make the request and then get the image. And the best part is that, this services is self hosted as well. You can easily host it on a VPS or on any online server.
There are some graphs and diagrams maker websites that you can use for the same. But if you need an API to do this from your web and mobile applications then Kroki is a very good option. You just give it the graph in text form and it will make the diagram from it which you can get using tools like cURL, HTTP, etc. There are no heavy dependencies are needed to install this. You can easily install it from its binary releases or using Docker. It can even work on local server and you can use it on a LAN in your office or home.
In this way, you can use this simple text to diagram API to easily convert text diagrams to images. And the method of doing that is very simple. If you go for the self hosting option, then there will be nothing different in the syntax of making the request to the endpoint.
Converting text diagrams using an API seemed pretty amazing and I liked the way it does it. There are a lot of examples for this are available that you will like. Different endpoints are available for creating charts, activity diagram, network diagram, block diagram, UML diagram, and some others. You can see the syntax for different type of text diagrams on its documentation page and then use them.
NOTE : We can convert the text into any desired language. For Example Japanese, Russian, Hindi. But the only condition is that the googletrans should recognize the destination language. Also, pyttsx3 will speak only the languages which are recognized by it.
The Graphviz layout programs take descriptions of graphs in a simple text language, and make diagrams in useful formats, such as images and SVG for web pages; PDF or Postscript for inclusion in other documents; or display in an interactive graph browser. Graphviz has many useful features for concrete diagrams, such as options for colors, fonts, tabular node layouts, line styles, hyperlinks, and custom shapes.
There are many OCR tools online that will let you extract text from images on any device. All you need is a browser and an internet connection to start using this tool (on both PC and mobile). I have tried many online OCR tools, and New OCR gave the best results for all the images I used. The service is completely free and very easy to use.
Although it worked fine for most of the images I tested and correctly extracted text with minor formatting mistakes, it really messed up one of the receipt images. The font size and color was completely different from the image that made it look very ugly. Thankfully, such an error can easily be fixed by selecting all the text and choosing a default font.
There are many apps for Android that let you convert images to text. Not only that, but you can also scan text on the go as all Android phones have built-in cameras. Text Scanner is my favorite Android OCR app as it lets you extract text from images offline. It also offers unlimited scans for free in multiple languages.
There is a button at the top-right corner of the app to select images from the gallery and a button at the bottom-right corner to use the camera to take a text photo. Use any of these options to upload the photo, and the app will automatically process and show the extracted text. You can switch between text and image using the buttons at the bottom to compare them.
If you particularly want to extract text from images on the web, then a Chrome extension can help. I like two extensions for this purpose, Copyfish and Project Naptha. Project Naptha is my favorite among the two as it automatically makes all text inside the images on the web selectable.
If you want to scan and convert images on demand instead, then Copyfish is a much better option. After installing Copyfish, you can click on the extension button to open up a tool to select the location of the text you want to extract. Once the area is selected, Copyfish will copy a picture of the highlighted area in its interface and then use OCR to extract text.
The tool comes with a trial version that gives access to all the features for 10 days. If you like the tool, you can buy one of the pro versions depending on your need. You can use Readiris to extract from images/PDFs saved on your PC, or take screenshots of any image and extract text from it.
Apart from extracting, you can annotate PDFs, add voice comments, split/merge PDFs, add watermarks, save scans online, convert text to audio, and much more. If you want both an OCR tool and a PDF manager, then Readiris is worth an investment.
If you want a cheaper OCR tool for mac, then Picatext is worth a try too. For just $3.99, you can extract text from saved images or new screenshots. The extracted text is automatically copied to paste anywhere easily, and you even have the option to select the default font.
Azure Computer Vision uses text recognition to extract and recognize text information from images. The Read API uses the latest OCR recognition models, and is optimized for large, text-heavy documents and noisy images.
AI enrichment in Azure Cognitive Search can extract and enhance searchable, indexable text from images, blobs, and other unstructured data sources like the JFK Files. AI enrichment uses pre-trained machine learning skill sets from the Cognitive Services Computer Vision and Cognitive Service for Language APIs. You can also create and attach custom skills to add special processing for domain-specific data like CIA Cryptonyms. Azure Cognitive Search can then index and search that context.
A textual modeling tool supports the use of textual notations and languages to describe software models and automatically renders the corresponding graphical diagram from that textual description. Many of these textual modeling tools focus on UML but beyond text to UML, we have also text to ER, text to BPMN, text to architecture and even text to chatbot tools. This category of textual modeling tools is also known as diagrams as code (for similar reasons as many model-driven tools are renaming themselves as low-code tools).
The service can be called from your blog or web page (with the textual description as part of the URL) to automatically display the image when accessing it. As paid options, you can use your own namespace for the images or even install it on your own host. Several integrations with third-party tools are also available.
ZenUML is one of the latest tools to enter the market. Read why the author believed that ZenUML was needed when there were already so many other textual tools for UML sequence diagrams. In short, creating sequence diagrams with ZenUML is really fast even for complex diagrams. ZenUML is especially targeting Confluence users.
GraphUp covers Sequence Diagramas and Gantt Charts. You can create sequence diagrams from simple textual descriptions and turn them into SVG images. You can integrate your diagrams in Jira, Confluence, Notion, GitHub, and many other tools using the Copy URL button. There is a free version (with watermark) and paid plans.
Probably the most popular tool in this category, Structurizr DSL enables you to create software architecture models based upon the C4 model, using a textual domain specific language (DSL). The DSL allows you to create multiple diagrams in multiple output formats, from a single DSL source file. See all the modeling primitives you can use in your textual descriptions in this language reference.
More than an alternative different syntax/rendering to those mentioned so far, Kroki provides a unified API with support for a number of diagrams. Basically, in one tool (or better said in one API) you have all the model types you may want to create from the text. You can install it on your own machine or use Kroki as a free external service.
Quite a few tools that were part of this list seem to be now dead or at least abandoned, like SeedUML, EasyUML Editor : Simple DSL for sequence diagrams , Diagrammr, Quick Sequence Diagrams Editor (only for sequence diagrams), modsl, and even this code sample for Visual Studio that allowed describing class, use case and activity diagrams using simple textual descriptions (not updated since 2011).
Convert raw PlantUML text into a URL. The URL can then be copied into a browser in order to create the diagram image. This URL is useful when tryingto put PlantUML diagrams into documents or uploading them to other websites.
PlantText is a text-based tool for quickly creating clear UML diagrams that can be compared, versioned, and managed. Simply type PlantUML language into the editor and refresh the screen to produce a professional diagram. Save, export, or copy the image for use in your requirements or design documents.
We show that scaling a simple pre-training task is sufficient to achieve competitive zero-shot performance on a great variety of image classification datasets. Our method uses an abundantly available source of supervision: the text paired with images found across the internet. This data is used to create the following proxy training task for CLIP: given an image, predict which out of a set of 32,768 randomly sampled text snippets, was actually paired with it in our dataset. 2ff7e9595c
Comments