Convert PNG to EPS
High quality, full-color, fully automatic vectorization. Using AI.
No low quality, or 2-color tracing like other sites do.
High quality, full-color, fully automatic vectorization. Using AI.
No low quality, or 2-color tracing like other sites do.
PNG is a very common lossless¹ raster format that supports transparency. It is used primarily for non-photographic content such as logos, icons, diagrams, illustrations, and other similar digital artwork.
As a raster format, PNG encodes images as a uniform grid of pixels, each of which can be thought of as a small rectangle (usually a square) of a specified color. Taken together this grid of pixels looks like an image when viewed at its native size, but scaling a raster image to a larger size will result in a pixelated or blurry image.
¹ Lossless means that encoding an image and then decoding it again produces a result that is identical to the original.
EPS is a legacy vector format that is rarely used. It has capabilities similar to PDF, but is not as widely supported.
It can be useful for legacy software that supports EPS but not either SVG or PDF. The subset of EPS language features that we use in our output is also very simple to decode, so it can be useful if your use case includes further programmatic processing of the results.
You can drag and drop your image onto the dashed box above, or click on it to open a file selection dialog.
Once your image is uploaded, the vectorization process will start automatically.
The vectorization process is performed on our high-performance servers, to quickly produce a high quality result.
Once the process is complete you will be shown the result in an interactive viewer capable of zooming and panning, so you can inspect it in detail before downloading.
When you are done reviewing you can click the 'Download' button to fetch your result to your computer.
We offer a wide variety of export options and formats, including EPS, that allow you to tailor the result to your specific needs.
Vectorizing an image is easy for the human eye, but surprisingly hard for the computer. Most software that tries to do it produces poor results, with glaring defects. Shapes can be introduced in the result that should not be there, such as anti-aliasing artifacts, or shapes can be missing that should be there, such as small and/or faint features. Even when the shapes are correct, the curves that define the shapes can be poorly chosen. In some cases, the curves simply don't follow the original image very well. In other cases, there are too many curves, or the curves that are present are poorly placed, don't connect with matching tangents when they should, or are represented using the wrong type of curve (e.g., using a quadratic bezier when an elliptical arc would be better).
Each step in the vectorization process is complex and there are many different algorithms that can be used. Many of our competitors use old and simple algorithms that do not produce good results. Some of them only support 2-color vectorization, which significantly limits their usefulness. The Vectorizer.AI vectorization engine is based on our own proprietary research and uses a combination of deep learning and other techniques to produce the best results. Curves are chosen carefully and optimized to fit the underlying image as closely as possible.
We also identify typical shapes like circles, ellipses, rectangles, stars, and triangles and represent them explicitly as such. This makes the results look better, and makes them easier to edit.
A common simplifying choice made when developing a vectorization algorithm is to only support two colors (e.g., black and white). Products built on top of such algorithms are significantly less useful and versatile than full-color vectorization systems. Other systems support more colors but only by repeatedly running a 2-color algorithm on each color separately.
In contrast, the Vectorizer.AI vectorization engine was built from the ground up to support full-color vectorization, including transparency and partial transparency. The Vector Graph underlying our system seamlessly maintains consistency between adjacent shape boundaries while allowing the system to optimize the result for the best possible quality.
Vectorization comes in two main flavors: reconstructive and inspirational.
Reconstructive vectorization is the process of converting a bitmap image that was once created by rasterizing a vector original, into a vector image that is as close as possible to the original. The goal is to reconstruct the original vector art. It is most useful on logos, icons, and other digital graphics where the original vector art is not available.
Inspirational vectorization converts a photograph, painting, or other other similar raster image into a vector image that is inspired by the original, but does not necessarily attempt to reconstruct it exactly. It is more about capturing some artistic essence or spirit of the original, than to reconstruct a platonic ideal.
Our primary focus is on reconstructive vectorization, but we of course also support inspirational.
Most vector formats support embedding raster images inside of them. Doing so creates a 'fake' vector file since it doesn't change the image's fundamental pixel nature. With such results you still can't do things like scale them to a larger size without loss of quality.
So when converting from PNG to EPS, it is very important to actually vectorize the image. This process involves detecting the shapes in the image, fitting curves to them, and exporting the result as a true vector file. The end result does not contain any pixel data and can be scaled to any size without loss of quality.
At Vectorizer.AI, we only support true vectorization.
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