Technical FAQs

As digital processes become more commonplace, it’s more important than ever for organizations to have the tools in place to manage electronic documents effectively. The evolution of PDF viewing technology continues to provide new levels of flexibility for software applications. Now that HTML5 is capable of rendering PDF data within a conventional browser, developers are looking for new ways to make the viewing experience even more seamless. By embedding PDFs in HTML, they can continue to streamline document viewing and reduce the need for external software.

Why Embed a PDF in HTML?

Sharing a PDF online is far easier to do today than it was just a decade ago. For many years, the two most commonly used options were providing a link to download the file directly from a server or sending it as an attachment in an email. Once the file was downloaded, it could be opened and viewed with PDF reader software installed on a computer. This, of course, introduced numerous security risks that are associated with downloadable files and email attachments.

The widespread adoption of cloud storage has made it very convenient to share a PDF file and even manage who has access to it. And since most modern browsers can view PDFs without needing to download the file, providing a link is typically all that’s necessary to pass the file along.

While this solution is usually sufficient for the personal needs of an individual user, it’s not a practical option for even a small-scale business when it comes to public-facing document management. Organizations want to retain control over their files with respect to how they’re accessed and displayed. By embedding PDFs in HTML, they can keep their documents within their secure application environment where they have full control over how they’re managed, shared, and viewed. For developers looking to provide a seamless user experience, building options for embedded PDFs into their software is critically important.

The Value of an Integrated PDF Viewer

Since most modern browsers can utilize HTML5 to render PDF files, developers could lean on those capabilities without building a dedicated PDF viewer for their application. That decision will very quickly lead to some unpleasant complications, however. In the first place, they are leaving a lot to chance in terms of the viewing experience. Not every browser renders PDF files the same way, so it’s very possible that two different users could have two very different experiences when viewing a document. In some cases, that could mean nothing more than a missing font that’s replaced with an alternative. But in other cases, it could mean that the document doesn’t open at all or is missing important graphical elements.

This approach also forces users to make do with whatever PDF functionality is incorporated into their browser’s viewer. In most cases, that will mean subpar search performance, a lack of responsive mobile controls, and no annotation features. The browser may also have trouble with some of the less common PDF specifications, making it impossible for some users to even view a document.

By embedding a JavaScript-based PDF viewer into their application, developers can ensure that documents will display the correct way every time. Since the viewing is handled through a viewer embedded into the web application by default, it will be the same no matter what kind of browser or operating system is being used. A customizable viewer also allows developers to adjust the interface to permit or hide certain features, such as downloading or markup tools.

The open-source PDF.js library is a popular choice for many web applications, but it comes with a number of well-documented shortcomings. In addition to lacking key features like annotation, it also doesn’t support the entire PDF standard and does not provide a responsive UI for mobile devices. For developers looking to add more robust features, working with PDF.js often entails quite a bit of additional coding and engineering to build those capabilities from the ground up.

Embed PDFs in HTML with Accusoft PDF Viewer

Accusoft PDF Viewer takes the foundation of PDF.js and provides robust enhancements to meet the viewing needs of today’s applications. In addition to incredibly fast text search, expanded PDF standard support, and optimization for high-resolution displays, this lightweight SDK is also equipped with a responsive UI that adapts automatically to mobile screens. Developers can integrate essential mobile features like pinch to zoom quickly and easy, with no additional integrations or engineering required.

With no external dependencies or complicated server configurations, Accusoft PDF Viewer integrates into a web-based application with less than 10 lines of code. Once the viewer is in place, developers can embed PDFs in HTML and easily render them to provide a state-of-the-art PDF viewing experience regardless of the browser or device users have at their disposal. And since the UI can be customized to your application’s needs, there’s no reason to sacrifice control for the sake of viewing convenience.

Accusoft PDF Viewer is a JavaScript SDK that you can incorporate into your application environment quickly and easily to provide much greater viewing control and functionality than is possible with a standard browser viewer or base PDF.js library. If you’re planning to embed PDFs in HTML as part of your software solution, taking just a few moments to integrate versatile and responsive viewing tools can ensure a high-quality viewing experience. Download Accusoft PDF Viewer Standard Version today at no cost to see how easily it can transform your application’s HTML5 viewing potential.

For additional features like annotation, eSignature, and UI customization, contact one of our solutions experts to upgrade to Professional Version.

Barcode Xpress and ImageGear .NET.  Barcode Xpress is a leading barcode reading SDK. While it supports a variety of image formats, Barcode Xpress works with ImageGear to support even more obscure image formats. For example, Barcode Xpress does not support reading barcodes on PDFs. Combined with ImageGear, developers can support a myriad of image formats and PDFs. With Barcode Xpress & ImageGear working together, developers can integrate a barcode reader that can detect barcodes on almost any kind of document.

Barcode Xpress accepts images in multiple different object types, such as System.Drawing.Bitmap. Using the method ImGearFileFormats.ExportPageToBitmap we can easily take any image that ImageGear supports and export it to a System.Drawing.Bitmap object that we can then pass to Barcode Xpress. So, only a tiny amount of code is required to recognize barcodes with ImageGear .NET and Barcode Xpress. Below, we’ll show various ways to pass different types of images and documents to Barcode Xpress.

Image:

// Load the image into the page.
ImGearPage imGearPage = ImGearFileFormats.LoadPage(stream, 0);

// Export the image to a bitmap and pass that bitmap to Barcode Xpress
 Result[] results = barcodeXpress.reader.Analyze(ImGearFileFormats.ExportPageToBitmap(imGearPage));

PDF:

We need slightly more code for a PDF. First, we specify a page number when calling LoadPage. Second, we must dispose of the ImGearPage object after we’re done with it. 

// Load the specified page of the PDF as an ImGearPage object
ImGearPage imGearPDFPage = ImGearFileFormats.LoadPage(stream, pageNumber);

// Export the image to a bitmap and pass that bitmap to Barcode Xpress
Result[] results = barcodeXpress.reader.Analyze(ImGearFileFormats.ExportPageToBitmap(imGearPDFPage));

(imGearPDFPage as IDisposable).Dispose();

Now that we’ve explained the most important part, we’ll show you a simple console app that recognizes barcodes on a PDF using the method above. 

The code below assumes you’ve installed an evaluation or development license for both Barcode Xpress and ImageGear .NET.

using System;
using System.IO;
using Accusoft.BarcodeXpressSdk;
using ImageGear.Core;
using ImageGear.Evaluation;
using ImageGear.Formats;
using ImageGear.Formats.PDF;

namespace BXandIGDotNet
{
	class Program
	{
    	static int pageNumber = 0;
    	static string fileName = @"Path/To/Your/PDF..pdf";
    	static void Main(string[] args)
    	{
        	// Initialize evaluation license.
        	ImGearEvaluationManager.Initialize();

        	// Initialize common formats.
        	ImGearCommonFormats.Initialize();
        	// Add support for PDF and PS files.
        	ImGearFileFormats.Filters.Insert(0, ImGearPDF.CreatePDFFormat());
        	ImGearFileFormats.Filters.Insert(0, ImGearPDF.CreatePSFormat());
        	ImGearPDF.Initialize();

        	using (FileStream stream = new FileStream(fileName, FileMode.Open, FileAccess.Read, FileShare.Read))
        	using (BarcodeXpress barcodeXpress = new BarcodeXpress())
        	{
            	// Load the specified page of the PDF as an ImGearPage object
            	ImGearPage imGearPDFPage = ImGearFileFormats.LoadPage(stream, pageNumber);

            	// Export the image to a bitmap and pass that bitmap to Barcode Xpress
            	Result[] results = barcodeXpress.reader.Analyze(ImGearFileFormats.ExportPageToBitmap(imGearPDFPage));

            	(imGearPDFPage as IDisposable).Dispose();

            	// Print the values of every barcode detected.
            	for (int i = 0; i < results.Length; i++)
            	{
                	Console.WriteLine("#" + i.ToString() + " Value: " + results[i].BarcodeValue);
            	}
            	Console.ReadKey();
        	}
    	}
	}
}

Using Barcode Xpress and ImageGear in Other Languages & Linux

You can also use Barcode Xpress and ImageGear together outside of the .NET framework. Barcode Xpress supports several different programming languages and frameworks including .NET Core, Java, NodeJS, Python, C, and C++. All of which can be used on Linux. 

ImageGear for C/C++ also supports Linux. Barcode Xpress Linux, which is a C/C++ library, ships with a sample called “ReadBarcodesIG”, that shows how to integrate Barcode Xpress Linux and ImageGear for C/C++. You can find the sample code after downloading our SDK here! For more information on Barcode Xpress, visit our Developer Resources page on the website. In addition, you can also find more information about ImageGear .NET on its respective Developer Resources page as well.

The Accusoft engineering team is always exploring ways to improve PrizmDoc’s document processing capabilities. We regularly consult with our active customers to ensure that we’re focusing on features that will help them push the boundaries of innovation and deliver a better experience to end users.

That’s why we’re excited to talk about PrizmDoc’s new OCR API feature, which allows Independent Software Vendors (ISVs) to tap into the power of Accusoft’s industry-leading optical character recognition technology to enhance their application’s document processing capabilities.

Wait, What Is OCR Again?

Optical Character Recognition (OCR) is a technology that converts different types of documents, such as scanned paper documents, PDF files, or images captured by a digital camera, into editable and searchable data. At its core, OCR works by analyzing the graphical elements of a document and recognizing the patterns of characters or symbols present in it.

Initially, the OCR software segments the document into elements like lines or words and then further breaks them down into individual characters. Using machine learning and pattern recognition, it then matches these individual graphical components to their corresponding textual elements in a pre-defined character database. This process allows for the extraction of textual data from images, enabling digital storage and efficient searching, which facilitates streamlined management and utilization of information across various sectors.

Benefits of PrizmDoc’s OCR API

Building OCR features into an application is a time-consuming and expensive process. The technology behind OCR is not only quite sophisticated, but it also requires access to complex and evolving language libraries that allow it to identify text accurately. Obtaining the licenses for these libraries, incorporating them into a new OCR solution, and keeping them updated can be a challenge for developers who are unfamiliar with OCR processing.

With PrizmDoc’s OCR API, ISVs can easily incorporate OCR capabilities into their applications with a simple API call. We’re constantly updating our OCR features to add new languages and forms of character recognition, all of which can be rolled directly into software applications as part of the PrizmDoc API integration.

What Makes Accusoft’s OCR Different?

Accusoft has long been an innovator in processing solutions that incorporate OCR technology. Where many solutions offer only full-page recognition, our OCR products support zonal field recognition, which allows applications to focus on predefined form field types to extract key data like names, dates, emails, and identification numbers.

Zonal OCR significantly increases processing speed, allowing applications to extract data from documents more quickly. It also enhances accuracy since the OCR engine is only reading specific areas of the page instead of scanning the entire page.

Of course, if your application needs to OCR an entire page or document, our OCR technology is more than capable of doing so quickly and accurately. We support multiple Western and Eastern languages, including Central European, Cyrillic, Baltic, and Asian characters. You can even set confidence levels for recognition results to incorporate manual reviews into your document process.

Industry Applications of OCR Technology

Fintech Applications

By integrating OCR technology into Fintech applications, financial institutions can automate the extraction of data from physical or digital documents, such as invoices, contracts, and bank statements, eliminating manual entry and reducing errors. This not only saves time but also enhances accuracy and efficiency, facilitating quicker decision-making processes. It can also aid in compliance and auditing tasks by easily retrieving information from a vast array of documents. By incorporating OCR APIs, Fintech applications can significantly enhance the finance industry’s service quality, fostering a more data-driven and customer-centric approach.

Legaltech

When integrated into a Legaltech application, lawyers, paralegals, and other professionals can utilize OCR technology to swiftly convert scanned documents, agreements, and legal briefs into searchable text. This can significantly expedite research and case preparation, allowing legal practitioners to efficiently sift through large volumes of text to locate pertinent information. It also enables the creation of digital databases that can be easily navigated and organized, enhancing the retrieval of case-related documents and fostering a more streamlined approach to legal work, thereby saving time and resources.

Insurtech

For ISVs building solutions to support insurance companies, an OCR API can serve as a pivotal tool in modernizing and streamlining the processing of numerous document types, including claims, policies, and supporting paperwork. It facilitates the quick conversion of scanned documents and images to searchable text formats, which can automate data extraction and reduce manual handling, minimizing the risk of errors and expediting claim processing times. By automating a significant portion of administrative tasks, insurance companies can focus more on developing customer-centric strategies and solutions, fostering greater efficiency and effectiveness within the industry.

Govtech

Governments handle a vast array of documents – from forms and applications to historical records. By implementing OCR technology into a Govtech application, governmental agencies can automate the data extraction process, thereby drastically reducing manual labor and minimizing errors. This makes the archival and retrieval of documents more efficient, fostering transparency and ease of access to public records. Furthermore, OCR can aid in analyzing data from various documents to formulate better policies and decisions based on historical and current data trends. Ultimately, integrating an OCR API can pave the way for more streamlined, cost-effective, and citizen-friendly governmental operations, promoting inclusivity and digital literacy.

Expand Your Application’s Potential with PrizmDoc OCR API

Incorporating advanced OCR capabilities into your application is easier than ever with the release of PrizmDoc’s OCR API feature. To learn more about how you can quickly add full-page and zonal character recognition that supports multiple languages, talk to one of our PrizmDoc experts today.

Banks and financial technology (fintech) companies commonly use document life-cycle management solutions to make their back-office functions run more smoothly. To take full advantage of these systems, organizations must be able to transform documents into a format they can work with.

Seventy-six percent of companies surveyed plan to prioritize machine learning (ML) and artificial intelligence (AI) deployments in 2021. Despite increased uptake, however, there is still a great deal of confusion surrounding these advanced concepts. In order to understand how organizations hope to leverage ML and AI in their technology initiatives, it’s helpful to take a step back and examine how they work and how they differ from each other.

What Is Machine Learning?

Machine learning uses statistics-driven algorithms to find patterns in massive amounts of data. These algorithms are designed to improve over time as they process more data to enable more accurate outputs. Machine learning is widely used to produce predictive recommendations — companies such as Google, Netflix, and Facebook collect data about user behaviors and feed it into machine learning algorithms which then produce targeted search results, movie recommendations, or advertisements. 

The key to machine learning success is data. The more data available to ML algorithms — and the higher-quality this data — the better they’ll be able to identify patterns in current datasets and apply them to new data sources.

Most machine learning methodologies fall under one of two broad categories:

• Supervised Learning: Developers classify and label data to guide the algorithm’s inputs and outputs to ensure specific patterns are recognized. This method is time and resource intensive because it requires data scientists to capture, control, and curate data sources.
• Unsupervised Learning: This approach provides ML algorithms with unlabeled and unclassified data and allows them to identify patterns based on unique data characteristics. Developers don’t interfere with the learning and pattern recognition process, instead evaluating the outputs for accuracy and modifying code as needed.

Why Does Machine Learning Matter?

Machine learning helps organizations leverage the massive amounts of data they’ve accumulated. This information is drawn from a variety of sources, including disparate forms and documents, data produced through customer transactions and service calls, and the ongoing operational data produced by staff as they interact with IT resources.

Thanks to both the rapid uptake of cloud computing and availability of large-scale data collection and analysis tools, these data volumes are increasing exponentially. As a result, aggregate assessment is now critical — companies need a way to rapidly and reliably derive patterns from available data, and apply these patterns to predictive action.

This is the evolving role of machine learning. By creating, testing, and deploying ML algorithms capable of rapid pattern analysis and application it’s possible for companies to benefit from this continual data influx rather than being constrained by the bounds of traditional data evaluation. To facilitate this process, many next-generation software tools and services are either equipped with built-in ML frameworks or are capable of interfacing with them.

Key Machine Learning Applications

The applications of machine learning are vast, but they tend to produce the best results when paired with existing processes that supplement human efforts or automate low-value, but labor-intensive, functions in the workplace. In effect, it has the potential to do almost anything a human mind can do, given enough time. 

Improved Data Capture

Capturing data from internal documents and customer-submitted forms can be cumbersome and time-consuming. It can also lead to wasted time and effort if data is incorrectly entered, duplicated, or accidentally deleted. By pairing machine learning tools with forms processing solutions like Accusoft’s FormSuite for Structured Forms, developers can build applications that identify, collect, and capture key data more efficiently and accurately. For example, a robot process automation (RPA) bot can be set up to receive extracted form data from FormSuite and then populate that information into the appropriate fields within an application. This not only accelerates forms processing workflows, but also greatly reduces the risk of data entry error. Properly implemented, automated data capture can act as a springboard for improved data insight and decision-making thanks to improved accuracy and consistency. 

Streamlined Content Creation

By combining machine learning algorithms and data sources with document editing tools, it’s possible to streamline key processes such as the creation of complex, compliance-bound content. One in-practice example is the use of Accusoft’s PrizmDoc Editor within the LegalSifter contract review and creation platform. By pairing its AI technology with PrizmDoc Editor’s document assembly capabilities, LegalSifter was able to quickly locate repetitive clauses and suggest replacements to create an automated contract creation experience for end users. 

What Is AI, and How Does It Relate to Machine Learning?

The terms artificial intelligence and machine learning are closely related and often used interchangeably, but they’re not identical.

Artificial intelligence refers to technologies that are capable of performing tasks like photo recognition or data pattern analysis with similar (or better) outcomes than human beings. Machine learning refers to the creation, testing, and refinement of the algorithms needed to support AI tools. In many ways, then, ML functions as a distinct process that helps make AI possible.

As noted by Toward Data Science, it often helps to think of AI, machine learning, and deep learning like a set of concentric rings. The smallest, inner ring is deep learning, which helps inform the middle ring of machine learning by providing layered neural network structures that improve the process of pattern recognition. The final, outside ring is AI, which depends on both deep and machine learning to deliver real-world results. 

Artificial intelligence tools can be broken down into two basic types:

• Generalized AI: These tools are capable of solving problems bounded by a clear set of rules. Using the ML algorithms that underpin the larger AI structure, general AI applications can act on stimuli — such as a security alert from an IT network — and respond appropriately by creating and logging reports or looping in human agents. 
• Narrow AI: These solutions are designed to solve specific, small-scale tasks. Building on the security example from above, a narrow AI application might see tools responding to specific threat events such as DDoS or ransomware attacks by deploying targeted, defensive responses that close active sessions, capture attack data, and prevent future connections from the same IP address. 

In practice, narrow AI tools can outperform their human counterparts in completing specific tasks, but are unable to translate this expertise into applicable action at scale. General tools come closer to mimicking human intelligence but are still a long way from replicating the depth and breadth of human thinking.

Limitations of AI

Much has been made about the potential of AI technologies to take the place of human staff, leading to a generalized sense of worry about the future of these tools at scale. Recent research, however, found that substantial confusion remains around not only the deployment of AI but the definition itself. In fact, one study found that 40 percent of AI startups in Europe were not actually using AI. In some cases, increasing market interest in AI tools encouraged the use of this term to help startups capture attention, in much the same way that rapid cloud adoption spurred the creation of a host of “cloud” companies that offered nothing of the sort.

Uncertainty around AI itself, however, also plays a role in this disconnect. Given the massive potential of AI to help companies solve both specific and generalized problems, the term can be applied in almost any context and made to fit almost any description.

Unlocking the Future

After spending many years confined to research projects and future-focused technology articles, both machine learning and artificial intelligence are making their way into the applications and software companies are deploying every day. As developers look ahead to building the next generation of technology solutions, they must not only think about how they can better leverage ML and AI principles, but also how to implement features that take advantage of them.

Accusoft’s collection of versatile SDK and API integrations deliver powerful viewing and image processing capabilities that help applications streamline workflows and enhance productivity. To learn more about how Accusoft can help you enhance the workflow in your machine learning or artificial intelligence projects, contact us today.