MonkeyLearn is a text analysis platform that helps you identify and extract actionable data from a variety of raw texts, including emails, chats, webpages, papers, tweets, and more! You can use custom tags to categorize texts, such as sentiments or topics, and extract specific data, such as organizations or keywords.
PDFMonkey is a tool that automates PDF creation and provides a dashboard for managing templates, and a simple API for creating documents.PDFMonkey Integrations
PDFMonkey + Monkey LearnClassify Text in monkeylearn when Document Generated is added to PDFMonkey Read More...
PDFMonkey + Monkey LearnExtract Text in monkeylearn when Document Generated is added to PDFMonkey Read More...
PDFMonkey + Monkey LearnUpload training Data in monkeylearn when Document Generated is added to PDFMonkey Read More...
Gmail + Monkey LearnClassify Text in monkeylearn when New Attachment is created in Gmail Read More...
Gmail + Monkey LearnExtract Text in monkeylearn when New Attachment is created in Gmail Read More...
It's easy to connect Monkey Learn + PDFMonkey without coding knowledge. Start creating your own business flow.
Triggers when a document's generation is complete and successful.
Classifies texts with a given classifier.
Extracts information from texts with a given extractor.
Uploads data to a classifier.
Find a document in PDFMonkey.
Generate a new document
Monkey Learn is a machine learning platform that allows users to create predictive models without coding. The user simply has to input their data, select the features they want to use for prediction, and the Monkey Learn algorithm will automatically create a model for the user. One of the main benefits of Monkey Learn is that it does not require technical knowledge. There are many different algorithms and methods that can be used for prediction and many different applications of predictive modeling.
PDFMonkey is a web application that uses machine learning to transform scanned PDFs into editable formats. Currently, PDFMonkey offers three services. OCR, text-to-speech, and speech-to-text. The OCR service transforms the scanned PDF into an editable format such as DOCX or RTF. The text-to-speech service generates an MP3 file from the scanned PDF. Users can listen to their scanned PDFs on their smart phones and manage them through their music app. The speech-to-text service allows users to convert scanned PDFs into editable formats and generate speech files. It also gives them the option to save the text as an MP3 file.
The integration of Monkey Learn and PDFMonkey offers a multitude of new features to users of both platforms. For example, users of PDFMonkey can now use their OCR output as input for Monkey Learn’s language detection algorithm. This integration enables them to automatically translate scanned documents into another language through a single step after they have already transformed the document into a readable format.
The integration of Monkey Learn and PDFMonkey is beneficial for multiple reasons. First, it creates a translation workflow for PDFMonkey users that previously did not exist. Previously, if a user wanted to translate a document from English into Spanish, they would have to go through four steps. OCR the document, upload the OCR output to MonkeyLearn, manually create a custom language classifier, and delete all of the unnecessary labels from the classifier output. Now, PDFMonkey users can skip steps 2 and 3 by uploading their OCR output to MonkeyLearn and use the resulting classifier directly in the translation workflow.
Additionally, this integration brings a variety of classification capabilities to PDFMonkey users. For example, before the integration, PDFMonkey only offered an OCR service. Now, with integration, PDFMonkey users can use MonkeyLearn’s language detection classifiers as well as other classification purposes such as sentiment analysis. With this integration, PDFMonkey users can now create custom classifiers that automatically detect language as well as sentiment in scanned documents. In addition to creating custom classifiers, PDFMonkey users can also train machine learning models on their own dataset using MonkeyLearn’s public dataset.
Finally, this integration provides a variety of pre-trained classifiers on which PDFMonkey users can train their own machine learning models. Before the integration, PDFMonkey users had limited choice of classification methods because of the lack of available pre-trained models. With integration, they now have access to over 80 different pre-trained models including sentiment analysis, language detection, gender detection, face detection, clothing recognition, etc. This greatly increases the possibilities of customized machine learning models that PDFMonkey users can train with their own datasets using MonkeyLearn’s algorithms.
This integration offers many benefits to both MonkeyLearn and PDFMonkey users. Integration gives users of PDFMonkey access to new machine learning capabilities such as language detection, sentiment analysis, gender detection, face detection, clothing recognition, etc. It also eliminates tedious workflows that previously existed for these features on some platforms like manually creating custom language classes with the correct labels for each term in some cases (e.g., “table” vs “desk”. Finally, it provides access to pre-trained classifiers on which users can train custom classes with their own datasets using MonkeyLearn’s algorithms.
Integration also offers specific benefits to MonkeyLearn users. Using integration, developers can now utilize PDFMonkey’s large dataset for training custom machine learning models for their own projects with minimal effort. Additionally, developers can now use hybrid sputions for their projects by combining MonkeyLearn’s APIs with PDFMonkey’s OCR service for automatic translation into editable formats with minimal effort.
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