When you purchase through links in our articles, we may earn a small commission. SwifDoo PDF Pro gives you lifetime access to a full PDF editor, converter, and OCR tool for Windows for $34.99 (reg. Adobe Acrobat is still the name most people reach for when they need to edit a PDF, and its subscription price reflects that. SwifDoo PDF Pro gets you the same core toolset without the recurring fee: a perpetual lifetime license for Windows is on sale for $34.99 (reg. Expect the basics here too: splitting and merging files, editing text, and converting PDFs to and from Word, Excel, and image formats.

Give your documents a voice with Adobe's podcast generator Podcasts can be a great way to conveniently explore complex topics without getting bogged down in pages and pages of text. The ease of listening to the content while commuting, walking the dog or even doing the washing up can make it a more viable option for many than reading a long document. So, wouldn't it be great if you could easily convert your own written files into podcast-style audio that could help you work through the dense subject matter? This might sound fanciful, as creating audio content has always been an expensive and time consuming business, requiring equipment, hosts, and a way of editing it all into something professional. Well, Adobe is changing that, as it now offers a simple way to convert documents into full audio episodes, without the need for any additional equipment or expertise.

Try Adobe Acrobat for free today! If you lead a project or team, then you'll know how many moving pieces you constantly have to keep an eye on each day. One area where this can be time consuming and problematic is with document workflows. How can you ensure that everyone involved has signed off on a plan, or even that they're reading the same version? That's where PDF Spaces comes in, a key feature in Adobe Acrobat, which makes document review and approval a streamlined affair, rather than one where you feel like you're herding clouds.

Try out Adobe Acrobat for free today! If you regularly work with PDF files, then you'll know how important they are in the modern world. Whether it's contracts that need to be signed, reports to be amended, forms that require filling out or that all important presentation, PDFs are a cornerstone of professional life. It can be surprising then that many people try to edit them with basic, free software that makes the process harder and more complicated. Formatting issues, problems moving from one device platform to another, and restrictive tools are commonplace, which can leave users frustrated at the time they're wasting.

When you purchase through links in our articles, we may earn a small commission. Adobe Acrobat's monthly fee is easy to resent and hard to justify when you're mostly editing, converting, and signing documents you could handle with something far more affordable. One license covers two devices simultaneously -- use it on your main machine and a secondary one. Updates are included, and access never expires. At $39.99, that's one payment to replace Adobe Acrobat -- a tool most people rent month-to-month for far more.

This paper introduces CommonForms, a web-scale dataset for form field detection. It casts the problem of form field detection as object detection: given an image of a page, predict the location and type (Text Input, Choice Button, Signature) of form fields. The dataset is constructed by filtering Common Crawl to find PDFs that have fillable elements. Starting with 8 million documents, the filtering process is used to arrive at a final dataset of roughly 55k documents that have over 450k pages. Analysis shows that the dataset contains a diverse mixture of languages and domains; one third of the pages are non-English, and among the 14 classified domains, no domain makes up more than 25% of the dataset. In addition, this paper presents a family of form field detectors, FFDNet-Small and FFDNet-Large, which attain a very high average precision on the CommonForms test set. Each model cost less than $500 to train. Ablation results show that high-resolution inputs are crucial for high-quality form field detection, and that the cleaning process improves data efficiency over using all PDFs that have fillable fields in Common Crawl. A qualitative analysis shows that they outperform a popular, commercially available PDF reader that can prepare forms. Unlike the most popular commercially available solutions, FFDNet can predict checkboxes in addition to text and signature fields. This is, to our knowledge, the first large scale dataset released for form field detection, as well as the first open source models. The dataset, models, and code will be released at https://github.com/jbarrow/commonforms

PDFs have long been the digital equivalent of a necessary chore: tedious, clunky, and often frustrating. But UPDF 2.0 flips the script, turning document work into something surprisingly smooth and efficient, and you don't have to pay a monthly subscription to get it. Right now, lifetime access to UPDF is just 59.99, a sharp 60 percent discount from the usual 149.99. Heads up: this deal is only for new users, and if you're eyeing UPDF's futuristic AI add-on, that's not included in the lifetime package--you'll have to grab it separately from UPDF.com. UPDF runs seamlessly across Windows, macOS, iOS, and Android, so whether you're on your laptop, phone, or tablet, your PDFs are always within reach.

Acrobat Pro's comprehensive PDF features show why it's still the editor against which all others are judged. Editor's note: This review was updated December 9, 2024 to reflect the addition of AI Assistant and current pricing. Adobe created the PDF two decades ago and its PDF editor has continued to rule the category, despite what many users felt was its exorbitant price. But a couple of years back, Acrobat adopted a cloud subscription model that now makes it more affordable for folks without an enterprise budget. Acrobat Pro is composed of three components: Acrobat, which allows you to perform a variety of editing functions on your PDFs on desktop and mobile devices; Adobe Document Cloud, which lets you create and export PDF files, as well as store and send files and collect electronic signatures; and Acrobat Reader, which enables you to read, print, and sign PDFs.

Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs). However, accurately assessing the performance of these applications remains a challenge, mainly due to the lack of suitable benchmarks that effectively simulate real-world scenarios. To address this challenge, we introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products to help evaluate the performance of existing models on domain-specific product QA tasks. Additionally, we propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task. Our experiments demonstrated that inducing domain knowledge through query reformulation allowed for increased retrieval and generative performance when compared to standard RAG-QA methods. This improvement, however, is slight, and thus illustrates the challenge posed by the datasets introduced.