Novitora Blog

Inside the Novitora Conversion Engine

Novitora Team — Wed, 15 Jul 2026 00:00:00 GMT

What Happens When You Convert a File

When you drop a file on Novitora and download the result a few seconds later, it travels through several stages: an encrypted upload, a bot check, a queued job on our processing engine in Frankfurt, and a short-lived download link that stops working once your file is deleted — within 2 hours for anonymous conversions. This post walks through that path so you know exactly what happens to your file and when it goes away.

Stage One: Upload

Everything starts in your browser. You pick a file (or up to 10 for a batch) and it uploads over an encrypted TLS connection. There is no account step and no email wall in front of it.

Two limits apply at this point. Each file can be up to 100 MB, and a single batch holds up to 10 files. The size check runs in the browser before the upload begins, so an oversized file is rejected immediately instead of wasting a round trip. Anything within the limits moves on to the next stage.

Stage Two: The Bot Check

Before a file reaches the engine, an invisible bot check runs in the background. Most people never see it. It only asks for interaction if the request looks automated, which keeps the free tools available for real users without putting a puzzle in front of every conversion.

Stage Three: The Server-Side Proxy

Your browser never talks to the conversion engine directly. The request goes to a server-side proxy on Novitora that validates the requested operation, attaches the file, and forwards it to the engine. Keeping the engine behind a proxy means the browser only ever sees Novitora, and the rules about what can be converted live in one place rather than being trusted from the client.

Stage Four: Processing in the Queue

The engine runs in the European Union, in Frankfurt. Incoming jobs join a queue and are processed in turn, which is what lets the service stay responsive when many people convert at once instead of trying to run everything at the same instant.

Each job is a single transformation: a PNG becomes a JPG, a PDF is run through OCR, a batch of images is bundled into one archive. The engine picks the right tool for the pair you asked for, does the work, and writes the output.

Stage Five: Download, Then Deletion

When the job finishes, the proxy hands your browser a link to the converted file. That link is signed and time limited: it stops working as soon as the file behind it is deleted.

The file itself is on a short clock. For anonymous conversions, converted files and their sources are removed within 2 hours of upload; authenticated API workspaces keep files for 24 hours. There is no long-term storage to opt out of, because there is no long-term storage. If you need the result, download it during your session and keep your own copy.

Why It Is Built This Way

Three constraints shaped the design:

No accounts, so no data to protect long term. The 2-hour window means files do not accumulate.
EU processing. Conversions run in Frankfurt rather than being scattered across regions.
Encrypted in transit. Uploads and downloads use TLS.

Novitora is early, and there is more on the roadmap, including a public API for teams that want to convert files programmatically. For now the engine powers the free web tools, from the image converter to OCR for PDFs and text to speech. Everything above happens every time you use one of them.

How to Turn SVG into Crystal Clear JPG for Your Slides

Novitora Team — Fri, 20 Feb 2026 00:00:00 GMT

Why SVG Breaks in Slide Decks

SVG keeps logos and icons crisp at any size on the web, but presentation software handles it inconsistently. Drop an SVG into PowerPoint, Google Slides, or Keynote and you may get missing fonts, broken gradients, or a blank placeholder. Converting to JPG fixes the compatibility problem, and with the right resolution, color, and compression settings the JPG looks as sharp as the vector did. This guide covers those settings and how to batch a whole deck at once.

Common SVG Problems in Presentations

Support for SVG rendering varies by tool and version. You might see missing fonts, broken gradients, or clipped elements, and some older versions of PowerPoint do not support SVG at all. Even when an SVG renders, a complex one with hundreds of paths and filters can slow down slide transitions because the software recalculates geometry each time.

Why JPG Solves It

Every presentation tool, operating system, and projector handles JPG without complaint. The trade-off is a fixed resolution and lossy compression, so the whole job is choosing settings that keep the image sharp at the size it will be shown.

Choosing the Right Resolution

Resolution is the decision that matters most. Too low and the image looks pixelated on a projector; too high and the deck balloons to hundreds of megabytes.

On-screen (laptop or 1080p monitor): export at 2x the size the image occupies on the slide. A 600x400 slide image should be exported at 1200x800.
Projectors (1080p or 4K): target at least 150 DPI at the slide's dimensions. For a 10 x 7.5 inch slide that is about 1500x1125 pixels.
Printed handouts or posters: go to 300 DPI for sharp output on paper.

When you convert with Novitora, set the output dimensions or DPI to match the target above rather than accepting the SVG's default size.

Managing Color Accuracy

SVG can use any color space (sRGB, Display P3, or CMYK via an embedded profile). JPG is effectively sRGB, so colors can shift during conversion if the source profile is not sRGB. Export from sRGB source art where you can, so the blue in your logo stays the same blue on a Mac, a Windows PC, and a projector.

Transparency needs a decision too. JPG does not support it, so any transparent area has to be flattened onto a background color. Novitora fills transparent regions with white by default, or with a color you choose. On dark slides, set that background color to match your slide theme so a light icon does not sit on a white box.

Batch Converting a Whole Deck

A single deck often has 15 to 30 graphics: icons, diagrams, screenshots, and charts. Converting them one at a time is slow and inconsistent.

With Novitora you can drop up to 10 SVG files at once and convert them with the same output format and settings, so every image shares the same resolution, quality, and background color. Each file can be up to 100 MB, which is far more than any icon needs. A practical routine:

Group the files by slide section (intro, features, data, closing) so they are easy to place afterward.
Pick one set of settings: resolution or DPI for the target display, quality in the 85 to 92 range, and a background color for transparency.
Convert the batch in one pass with the SVG to JPG converter.
Spot-check a few outputs at full zoom before importing the set into your slide tool.

Compression Without Visible Artifacts

JPG quality runs from 1 to 100 and trades file size against fidelity. For presentation graphics:

95 to 100: visually lossless, large files. Keep this for archival or print.
85 to 92: excellent quality with artifacts invisible at viewing distance. This is the range to use.
70 to 84: compression becomes visible on close inspection; fine for thumbnails.
Below 70: visible banding and halos. Avoid it for anything professional.

If a graphic still looks soft, the fix is usually more resolution, not less compression. Text-heavy images in particular benefit from exporting at a higher pixel size before the JPG compression is applied.

The Short Version

Converting SVG to JPG for slides comes down to four choices: resolution that matches the display, an sRGB color path, a background color for transparency, and quality around 85 to 92. Set those once, batch the deck, spot-check the output, and you avoid the export-test-re-export loop that eats preparation time.

How AI Is Transforming Document Conversion in 2026

Novitora Team — Wed, 18 Feb 2026 00:00:00 GMT

How AI Changed Document Conversion

For years, converting a document meant rigid template matching that broke on anything unusual. Machine learning changed the approach. Modern conversion reads a page the way a person scans it, recognizing regions, structure, and text together instead of one character at a time. That shift shows up most clearly in three places: OCR, layout detection, and format inference.

OCR That Reads Regions, Not Just Characters

Traditional OCR steps through a page character by character and stumbles on unusual fonts, low-resolution scans, and complex layouts. Newer models based on vision transformers take in whole regions of a page at once, using surrounding context to resolve characters that would be ambiguous in isolation.

The practical gain is on the hard inputs: tables, multi-column layouts, and mixed-language documents, where reading order and context matter as much as the individual glyphs. Novitora offers OCR for PDFs so a scanned document comes out with a real, selectable text layer instead of a flat image.

If you want to know how much better a given engine is on your own files, measure it. Character Error Rate is the standard way to compare OCR output against a known transcript, and it beats trusting any headline accuracy figure.

Layout Detection and Preservation

Preserving the original layout is one of the harder problems in conversion. Models can now detect headers, footers, sidebars, floating images, and nested tables, then rebuild them in the target format.

The difference is easy to see when you convert a complex PDF report to Word. Instead of a flat wall of text, headings stay headings, tables keep their structure, and images stay anchored where the author put them. The structure survives the trip rather than being flattened into paragraphs.

Smarter Format Inference

Converting a spreadsheet to CSV used to mean cleaning up afterward: fixing header rows, correcting date columns that came through as plain text, and unrolling merged cells by hand. Models that read the shape of the data can infer which rows are headers and how columns should be typed, which cuts down the manual cleanup that used to follow every job.

Where This Is Heading

The next step is domain awareness. A legal brief, a medical record, and a financial statement each have their own conventions, and conversion that understands those conventions can apply rules tailored to the document type. For now, the reliable move is to pick a tool with strong OCR and layout handling, then check the output against the metrics that matter for your work.

The Complete Guide to Batch File Conversion Workflows

Novitora Team — Thu, 12 Feb 2026 00:00:00 GMT

Convert Many Files at Once

If you convert files one at a time, the work scales with the pile. Batch conversion breaks that link: you set the target format and options once, then process a whole selection together. With Novitora you can convert up to 10 files in a single batch, free and without an account. This guide covers how to run a clean batch today and the habits that keep large jobs from turning into a mess.

Run a Batch on Novitora Today

The web workflow is short:

Open the converter for the pair you need, such as the image converter.
Drag in up to 10 files at once. Each file can be up to 100 MB.
Set the output format and any options (quality, dimensions, DPI) for the whole selection.
Start the batch, then download the results. When a batch produces many outputs, they come back bundled so you get one download instead of ten.

That covers the common case: a folder of images or documents that all need the same treatment. For very large or recurring jobs, the habits below matter more than the tool.

Name Files So You Can Trace Them

Consistent naming prevents confusion when you are staring at a hundred outputs. A pattern that holds up is:

{original-name}_{format}_{date}.{ext}

For example, annual-report_pdf_2026-07-08.docx. When something looks wrong three steps later, the name alone tells you what it was and when it was made.

Split the Work Into Passes

Rather than one enormous, mixed batch, group files by the conversion they need and run each group as its own pass. Images to WebP in one pass, PDFs through OCR in another. Passes are easier to check, and if one type has a problem you have not held up the rest.

Check the Output, Do Not Assume It

Not every file converts cleanly, so build a quick check into your routine:

Compare each output's page count or dimensions against the source to catch anything truncated.
Spot-check a few files from every batch by opening them, not just glancing at the file list.
Keep the originals until you have confirmed the outputs, since converted files on Novitora are removed within 2 hours.

What Is Coming

Everything above is a manual web workflow, which fits most one-off and weekly jobs. For pipelines that need to run on their own (a watched folder or a scheduled job), a public API is on the Novitora roadmap. Until it ships, the 20-file batch in the web tools is the way to move a lot of files quickly.

PDF Accessibility: Best Practices for Inclusive Documents

Novitora Team — Thu, 05 Feb 2026 00:00:00 GMT

Why PDF Accessibility Matters

An untagged PDF is, to a screen reader, a picture of text: unreadable. Making converted documents accessible means preserving structure, adding alternative text, fixing reading order, and using real text instead of scanned images. It also keeps you on the right side of rules like the ADA, Section 508, and the European Accessibility Act. This guide covers the practices that matter and how to verify them after a conversion.

PDF/UA and WCAG in Brief

PDF/UA (Universal Accessibility) is the ISO standard for accessible PDFs. It calls for tagged content, a correct reading order, alternative text for images, and proper structural elements such as headings and lists.

WCAG 2.2 covers web accessibility broadly and applies to downloadable documents too. Level AA is the widely accepted minimum to aim for.

Preserve Document Structure

When you convert from Word or HTML to PDF, the heading levels, lists, and table structures need to carry through as tags. A flat, untagged PDF reads as one undifferentiated block to assistive technology.

Start from a well-structured source: use real heading styles rather than large bold text, mark lists as lists, and build tables with header rows. Structure that exists in the source has a chance of surviving the conversion; structure that was only visual has none.

Add Alt Text to Every Meaningful Image

Every non-decorative image needs descriptive alternative text, and purely decorative images should be marked as such so screen readers skip them. Add alt text in the source document before you convert, since that is where you have the context to describe each image well.

Get the Reading Order Right

Multi-column layouts, sidebars, and callout boxes can scramble the order in which a screen reader announces content. After converting, navigate the document with a screen reader and confirm it moves through the content in a logical sequence rather than jumping around the page.

Use Real Text, Not Images of Text

Scanned documents are the most common accessibility failure, because the "text" is just pixels. Run scanned pages through OCR so the words become a selectable, readable text layer. Novitora's OCR for PDFs produces that text layer, which is the prerequisite for everything else on this list.

Test the Output, Do Not Trust It

Conversion does not guarantee accessibility, so verify it:

Run the file through a checker such as PAC (PDF Accessibility Checker) or Acrobat's built-in accessibility check.
Fix what the automated tools flag: missing tags, absent alt text, uncertain reading order.
Finish with a manual pass using an actual screen reader, which remains the most reliable test.

Automated checks catch the majority of problems, but they cannot judge whether your alt text is useful or your reading order makes sense. That part is still a human review, and it is worth the time.

Automate Email Attachment Conversions with Novitora

Novitora Team — Tue, 20 Jan 2026 00:00:00 GMT

The Time Sink of Email Attachments

Attachments arrive in whatever format the sender chose. Turning a stack of emailed PDFs, images, and documents into the formats your team actually uses is repetitive work that adds up fast. You cannot yet automate that end to end with Novitora, but you can make the manual version much faster and set yourself up for the automation that is coming. Here is a workflow that works today.

A Faster Manual Workflow

The goal is to cut the number of steps between "attachment lands in your inbox" and "file is in the right format." A batch-friendly routine:

Save the attachments you need to convert into one folder as they come in during the day.
Once you have a set, open the right converter, such as PDF to Word for editing or OCR for PDFs for scanned pages.
Drag in up to 10 files at once and convert the whole set in one pass.
Download the results and move them where they belong.

Batching the conversion instead of doing it per email is where the time savings come from. Ten attachments become one trip through the tool rather than ten.

Match the Tool to the Attachment

A few pairings cover most inboxes:

Contracts and reports sent as PDF that you need to edit: convert with PDF to Word.
Scanned or photographed documents with no selectable text: run them through OCR for PDFs so the text becomes searchable.
Design assets and screenshots in mixed formats: send them through the image converter to standardize on one web-ready format.

What Happens to the Files

Attachments you convert are handled the same way as any other Novitora upload. Transfers are encrypted, processing runs in the European Union, and converted files are removed within 2 hours. Nothing is kept beyond that window, so download what you need during your session.

What Is Coming

Rules that watch an inbox and convert attachments on their own need a connection to your email provider, which Novitora does not offer yet. A public API that would make that kind of automation possible is on the roadmap. Until then, the batch workflow above is the fastest honest way to keep attachment formats under control.

How to Measure File Conversion Quality: Key Metrics

Novitora Team — Wed, 14 Jan 2026 00:00:00 GMT

How to Measure Conversion Quality

Conversion quality is measurable, not a matter of taste. Four metrics cover most cases: character error rate for text and OCR, structural similarity for anything that must look the same, layout fidelity for structure, and the output-to-input size ratio for efficiency. Run them against a fixed test set and you can compare tools objectively and catch regressions early.

Start With a Fixed Test Corpus

You cannot compare numbers that come from different inputs. Before measuring anything, assemble a corpus you will reuse for every test: 50 to 100 documents that mirror your real workload. Mix contracts, reports, spreadsheets, scanned pages, and presentations so the scores reflect the files you actually convert.

Keep the corpus under version control and never edit it once you start benchmarking. When you re-run the tests after a tool update, the only variable that changed is the tool, so any score movement is real signal.

Character Error Rate for Text and OCR

For anything that involves OCR or text extraction, character-level accuracy is the metric that matters. Character Error Rate (CER) compares the extracted text against a known-correct transcript:

CER = (substitutions + insertions + deletions) / total characters in the reference

You compute it with an edit-distance (Levenshtein) function. Python's jiwer library reports CER and Word Error Rate directly if you feed it the reference and the hypothesis strings.

As a working rule of thumb, teams often treat a CER under 1% as strong for clean printed text and expect higher rates on skewed scans, handwriting, or noisy backgrounds. Report the number per document type rather than as a single blended average, because a good score on clean PDFs can hide a poor one on photographed receipts.

Structural Similarity for Visual Fidelity

Some conversions have to look identical to the source: PDF to image for previews, or a document to PDF for print. Pixel comparison quantifies the difference. The Structural Similarity Index (SSIM) returns a value from 0 to 1, where 1 is a perfect match.

To measure it, render the source and the output to images at the same resolution, then compare them:

Python: skimage.metrics.structural_similarity from scikit-image.
Command line: magick compare -metric SSIM original.png converted.png diff.png from ImageMagick.

An SSIM above 0.95 usually reads as near-identical to the human eye. Below that, open the diff image the tools produce and look at where the mismatch concentrates, since a low score is often caused by one shifted element rather than uniform degradation.

Layout Fidelity for Structure

When the output format keeps structure (PDF to Word, HTML to PDF), check that the structure survived. This is a scripted comparison, not a visual one:

Headings: same count of H1, H2, and H3 elements, in the same order.
Tables: row and column counts match, and merged cells are handled the way you expect.
Lists: ordered and unordered lists keep their nesting and numbering.

Parse both files, extract these counts, and diff them. A conversion that turns a structured report into a flat wall of text will fail this check even when it looks acceptable at a glance.

Output-to-Input Size Ratio

Divide the output file size by the input size and track the result. For a format upgrade such as BMP to WebP you should see real compression, so a ratio well under 1 is the goal. For same-fidelity conversions such as DOCX to PDF the ratio should stay close to 1. A conversion that doubles file size is quietly wasting storage and bandwidth, and a sudden change in the ratio after a tool update is worth investigating.

Latency and Throughput

Speed is easy to measure yourself: record wall-clock time from the moment you submit a file to the moment the output is ready, and log it per file. Report percentiles, not just the average, because the p95 (the slowest five percent) is what your users feel on a bad day. Run the same corpus a few times to smooth out network noise before you trust the numbers.

Turn the Metrics Into a Dashboard

Aggregate the scores so degradation is visible before anyone complains:

Daily averages for CER, SSIM, and layout fidelity per document type.
Outlier alerts when any metric drops below a threshold you set.
Trend lines to catch slow drift, such as a library update that gradually worsens table handling.

Benchmark Novitora Against Your Own Corpus

The most honest number is the one you produce on your own files. Novitora is free and needs no signup, accepts files up to 100 MB, handles up to 10 files per batch, and includes OCR for PDFs. Run your corpus through it, score the output with the methods above, and compare the results against whatever you use today. Your measurements will tell you more than any headline claim.