Technical FAQs

Despite the explosive growth of big data and sophisticated analytics platforms, a 2019 study by Deloitte found that 67 percent of business leaders are not quite comfortable using them to inform decision making. For many organizations, spreadsheets remain the preferred tool for managing data and evaluating trends. Developers looking to build the next generation of business applications can accommodate those tendencies by integrating native spreadsheet support for Microsoft Excel workbooks.

Excel Worksheets vs Excel Workbooks

Although sometimes referred to interchangeably or described broadly as spreadsheets, there is a key distinction between an Excel worksheet and an Excel workbook. A worksheet consists of only one spreadsheet while a workbook contains multiple different spreadsheets separated by tabs.

The difference may not be very important when viewing or sharing XLSX files natively in Microsoft Excel, but it can create serious challenges when rendering those files in another application. Without some way of accurately rendering dynamic spreadsheet data, viewers are often forced to resort to a static print preview image. This process makes the file viewable, but also leaves it “flattened” because all interactive elements are removed from the spreadsheet cells.

If the workbook contains worksheets with linked data (that is, cell data from one sheet is affected by cell data from another sheet), it’s critical that a viewing solution preserves the dynamic aspects of the file. The advantage of a spreadsheet is that it can serve as a working document. Without the ability to interact with it, users might as well simply copy and paste the data into a text document.

Managing Excel Workbooks with PrizmDoc Cells

PrizmDoc Cells provides several options for managing Excel workbooks, making it easy to transition back and forth between XLSX format and web browser viewing. Once a proxy route is set up within the application to send API calls to the PrizmDoc Cells server, three different commands can be used to manage Excel workbooks.

Upload Workbook

This API call adds a new XLSX file for viewing and editing. When a document is uploaded to the system, the server assigns a unique workbook ID to it so it can be found and rendered in the application’s viewer in the future. After uploading a workbook, a new session can be created using the workbook ID for viewing and editing purposes. 

Download Workbook

When PrizmDoc Cells displays a spreadsheet, it renders the XLSX file itself, but it doesn’t make any alterations to that file. As each session makes edits to the workbook, those changes are associated with the document ID rather than the original XLSX file, which preserves the integrity of the original spreadsheet. At some point, however, those edits may need to be saved into a new Excel workbook. 

The download API call converts the current session document so it can be downloaded as an XLSX file. File availability can be set during the download process to control who will have access to the new workbook.

Delete Workbook

Old versions of workbooks often need to be deleted for security reasons, usually because they contain confidential data. Since the original XLSX file remains safely within application storage, there often isn’t much sense in retaining workbooks IDs that aren’t being used. The delete API call removes a workbook ID from the server. Once removed in this way, the workbook cannot be viewed, edited, or downloaded by PrizmDoc Cells.

Preserving Workbook Functionality

Since PrizmDoc Cells natively renders information contained in an XLSX file, it retains the dynamic elements that make spreadsheet workbooks so useful to organizations. Not only does it preserve proprietary business logic and formulas, but it also maintains the integrity of this information across multiple worksheets. Cell content can still be searched to quickly locate important text or data throughout the workbook.

For situations where proprietary formulas need to be protected, PrizmDoc Cells allows users to upload XLSX workbooks as values-only files, with all spreadsheet formulas removed. Also, any cells locked in an uploaded XLSX file will remain locked in PrizmDoc Cells to preserve workbook security.

True Spreadsheet Workbook Support for Your Applications

Many organizations continue to depend upon spreadsheet workbooks to manage their business. By providing feature-rich workbook support within their applications, developers can help them retain control over their proprietary spreadsheet formulas without sacrificing the functionality they expect from Excel. 

PrizmDoc Cells makes it easier than ever to share spreadsheet workbooks without having to rely upon Microsoft Excel dependencies. Shared XLSX files can remain safely within a secure application environment to prevent unauthorized downloads or troublesome version confusion. Get a first-hand look at how PrizmDoc Cells can enhance your application in our extensive online demo.

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.

The insurance market is booming. As noted by research firm Deloitte, the property and casualty (P&C) sector saw a massive income uptick in 2018 and steady growth last year that’s predicted to carry forward through 2020. To help manage the influx of new clients and handle more claims, many firms are spending on insurance technology (insurtech) — digital services and solutions that make it possible to reduce error rates and enhance operational efficiency. InsurTech SDKs are important components of this transformation.

Both in-house insurtech solutions and third-party platforms often excel in specific areas but come up short in others, putting insurance firms at risk of writing off potential gains. While solution switching and ground-floor rebuilds offer one route to success, there’s another option that’s more custom to your business needs: software development kits (SDKs). Here’s a look at three top SDKs that offer customized functionality potential.

FormSuite for Structured Forms: Solving for Data Capture

Time is money. The faster insurance companies accurately complete and file documents, the greater their revenue potential. And as noted by KPMG, the need for speed is more pressing than ever. Many insurance sectors have seen substantial increases in both claims and new applications as the COVID-19 crisis evolves. 

As a result, accurate and agile forms processing is critical to keep up with demand. If current insurance software can’t quickly capture forms data, recognize standard form fields, and let users easily create standard form libraries, policy processing falls behind.

FormSuite for Structured Forms makes it easy for developers to build in form identification and data capture that includes comprehensive form field detection with OCR, ICR, and OMR functionality and the ability to automatically identify scanned forms and match them to existing templates.

ImageGear for .NET and C/C++: Simplifying Conversion

Conversion is critical for insurance firms. Depending on the type and complexity of insurance claims, companies are often dealing with everything from Word documents for initial client assessments and .GIF or .JPG images of existing damage to contractor-specific PDFs or spreadsheets that detail necessary materials, time, and labor costs. The result? A mash-up of multiple file types that forces adjusters to spend valuable time searching for specific data instead of helping clients get their claims process up and running. This makes it difficult to recognize value from emerging digital initiatives. 

Accusoft’s ImageGear for .NET and ImageGear for C/C++ empower developers to integrate enterprise-class file viewing, annotation, conversion, and image processing functions into existing applications, allowing staff to both quickly collaborate on key tasks and find essential data across a single, easy-to-search document.

ImageGear: Streamlining PDF Capabilities

While insurance technology offers substantive opportunities for end-users to capture, convert, and retain data, this technology can also come with the challenge of increased complexity. According to recent research from PWC, for example, firms looking to capitalize on insurtech potential must be prepared to rapidly develop new product offerings and embrace the expectations. 

As a result, companies need applications that streamline current functions and allow them to focus on creating cutting-edge solutions. For example, PDF is a file format that is still used by enterprises worldwide to maintain document format consistency and maximize security. When it comes to converting multiple files into a PDF, software can be expensive and introduce data security issues. 

This can all be solved with an SDK like ImageGear, which makes it possible to integrate the total PDF package into any document management application, both reducing overall complexity and freeing up time for staff to work on new insurance initiatives.

Insurtech forms the framework of functional futures in policy applications, claims processing, and compliance reporting, but existing software systems may not provide the complete capability set companies need to make the most of digital deployments. These top SDKs offer insurance IT teams the ability to integrate key services, improve speed, and boost security at scale. Learn more about Accusoft’s SDKs at www.accusoft.com/products. 

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.