Dr. Yecheng (Ted) Wu
President of Able Software Corp.
Able Software R2V for windows (9x, NT and 2000) R2V is the one of the fastest and finest automatic map vectorization software, which is one of the most powerful GIS friendly products. R2V combines the power of intelligent automatic vectorising technology with an easy-to-use, menu driven, and graphical user interface in the Microsoft environment. The software converts scanned maps or images to vector formats for mapping, GIS, CAD and scientific computing applications. The entire raster to vector conversion process is fully automatic and needs no human intervention. Powerful functions like editing and processing are provided to edit, geo-reference and label your data. R2v supports almost all images format (TIFF, GeoTIFF, JPEG, BMP, GIF, and RLC) including 1-bit bi-level, 8-bit gray scale and color images (4-bit, 8-bit and 24 bit). Powerful editing functions can easily update your data and label them geo-reference and the data is ready to use. Advanced image processing functions can also be implemented which includes such as image wrap or registration, image mosaic, color classification and separation and many more to handle different types of image. R2V creates 3D data set automatically from labeled line data and display them using R2V’s advanced 3D display and animation function. R2V provides a complete solution to digitize vector from various image sources. Thus a system easy to use and learn quickly by users with any level of technical background. Website: www.ablesw.com/r2v
WinTopo Pro from SoftSoft Ltd. WinTopo Pro is a high quality raster to vector conversion tool. This software is based on established research finding of Zhang-Suen and Stentiford methods as well s the Canny method of edge detection on raster vectorisation. WinTopo converts images from scanners and other digital sources into vector data suitable for CAD and GIS systems. WinTopo Pro supports image raster formats such as TIFF, GIF, Jpeg or BMP and processes into DXF and other vector file formats. It is one of the fast and efficient system having a powerful georeference feature that ensures the vectors will be scaled, rotated, translated and deskewed to the correct coordinates ready for AD/GIS/CNC system. WinTopo Pro incorporates comprehensive functions of both raster and vector editing and extraction. Website:
Draftsman Raster to Vector Conversion Series
Draftsman Series from Arbor Image Corporation Draftsman series first came into the market in 1990, running stand alone under DOS. Since then various upgrades and operating system modifications it has become well known in the CAD industry for high quality raster to vector conversion. Draftsman Series32-bit raster to vector conversion program. Simple to install, easy to use and extremely fast. It comes with impressive variety of editing functions such as stretch, scale, move, copy, delete, insert and join etc. The various series available from Arbor Image are:
Draftsman Cutting Shop: This series is especially available for all cutting and engraved applications. The program imports color, gray and black & white images. It has many features designed for NC applications such as line offset, cuttable fills, automatic and manual tool path sorting, show direction of cut and if required, reverse it. Also imports DXF files from low-end raster to vector programs. It imports and exports raster files (PCX, BMP, TIFF, RLC, CALS group, JPG) and vector files (DXF, PLT, IGES, DXB, CGM, HPG, TXT, 2D). Draftsman 2000: This software comes with complete full featured image conversion package, including automatic raster to vector conversion, optical character recognition, heads up digitizing, raster and vector editing and for achievers, vector to raster conversions also. A new feature in vector editing it merges two vector drawings into one. Scans of prints, sketches and silhouettes are quickly converted into vector drawings that look like original image. Draftsman for AutoCAD Release 14 and Release 2000: This series are fully integrated into AutoCAD running under 95, 98, NT and 2000. The 32 bit performance makes raster to vector conversion of D and E size drawings very fast and the tight integration allows Draftsman to vectorise all colour and black & white raster formats that AutoCAD supports. It has improved conversion preferences screen so that users can create their own list of preset sets of preferences. For example there can be a preset, named, parameters for conversion of TOPOgraphic maps, FLOOR plans and LOGOS. Website: https://www.arborimage.com/aihome.htm
Draftsman Series from Palisades Research, USA. Draftsman Plus 32 and Draftsman Plus Series is almost similar to the series available from Arbor Image. A full featured program for those who need the highest possible accuracy. This package combines ultra high quality automatic conversions with additional built in editing and OCR capabilities all in a Windows format. The unique feature that singulars it from Arbor series is it runs independent of AutoCAD and is not affected by AutoCAD updates. AutoImage-Professional (Ai-Pro) software gets your drawings off paper and directly into CAD. Ai-Pro converts scanned image sof your drawings automatically into fully vectorised CAD files that can be edited, zoomed and snapped to using normal CAD commands and other commands such as Double Accuracy Logic knows when to smooth an entity and when to closely adhere to every raster nuance.Ai-Pro converts raster files from virtually any scanner (RLC, PCX, IMG formats) into vector files that are easily imported into most CAD packages such as AutoCAD (DXF & DXB), CADkey (CDL), EasyCAD (EXF), Drafix (POR), DesignCAD (DC2), Intergraph (IGS & CGM), VersaCAD (.2D) and Generic CADD (DWG or GCD). Ai-Pro is a DOS based program that works with most CAD packages. Website: https://www.findthem.com/rvidx.htm
TracTrix Series from Trix Systems Inc. TracTrix 2000 has a unique option for raster to vector conversion which automatic converts raster lines to vectors as NURBS, Bezier curves, polylines, arcs or circles. It also has an option of optical character recognitionthat converts raster text into vector text entities. TracTrix can be taught to recognize your own special fonts and stencils. Trix RasterServer unique converter that converts popular CAD formats such as DXF, HPGL, DWG to PDF and multiple raster formats including BMP, CALS, C4 and TIFF. It automatically works in the background providing batch mode conversion to high resolution raster image for anyone on a network.. Outputs the latest PNG format for direct publication of images through Browsers and large drawings can be split into user-defined output sheet sizes. Website: https://www.trixsystems.com
Scan2CAD Series from Softcover International Limited The two versions of Scan2CAD – Scan2CAD and Scan2CAD OCR Plus are unique and the same time exactly the same program except that Scan2CAD OCR Plus includes as additional menu , the Plus Menu. This Plus menu contains commands for font training. Scan2CAD unique with its features well equipped from quick conversion of raster/scanned files to easy to edit DXF (vector) files, supporting all the major file formats for conversion, comprehensive raster and vector editing tools and unique OCR text recognition tool. The distinctive about Scan2CAD is the font training where it recognises text by means of technology called neural networks. Thus the software learns to recognise shapes and patterns from a range of examples. It works with any scanner, any size of drawing and any PC CAD program. It is one of the first professional raster to vector converter with OCR text recognition available at a budget price. Scan2CAD program thus marks a major turning point in scan conversion cost effectiveness. Website:
R2V: Automated Map Digitising
The Issues Involved in Automated Raster to Vector Conversion
Choose Image Type
Most good quality black and white maps and engineering drawings, including color map separates, can be scanned as 1-bit monochrome. If the background is clean and the scanned image does not show many dots and speckles, 1-bit monochrome is the perfect type to use because it takes less storage space and is faster in display and processing.
For single color maps with dirty and smearing background, such as old maps or blue prints, they can be scanned as 8-bit greyscale and cleaned using image-processing techniques, such as background removal. Noise and other artifacts can be easily smoothed out using a pair of grey level thresholds before automatic vectorisation. Greyscale image provide more information than 1-bit monochrome image for image processing tasks such as background and noise removal but is normally 8 times larger in size than 1-bit monochrome image. If smaller image size is preferred, one safe way is to start with greyscale image typed and uses software to clean up and convert to 1-bit monochrome for storage. On the other hand, image compression, such as JPEG and wavelet methods, can be applied to reduce the size of greyscale image while maintaining the same pixel bit depth.
Although color scanners have come a long way, large format and high resolution scanning is still quite expensive. If the source image is in color and a good quality color scanner is available, scanning using 24-bit color image type certainly gives the benefit of separating color layers and simplifying the vectorisation process. Color separation normally uses color classification or color ratio based methods to divide millions of colors into a limited number of color groups and each group is assigned a single color. Each color in the classified image can then be vectorised or extracted to create a single color image.
Other color images, such as satellite and aerial photos, have been used directly to create vector data, such as region boundaries, street and road lines. Because of more bits (normally 24-bit) are used, color image files are much bigger than 8-bit greyscale and 1-bit monochrome images and require more system resource to store and process. Of course, image compression techniques can help to reduce the size of color images. When using lossy compression, such as JPEG or wavelet-based methods, compression ratio must be carefully selected so image quality is not sacrificed since the success of vectorisation depends heavily on the quality of the image.
Preprocessing steps are different depending on the image type. For 1-bit monochrome image, de-speckle is often used to remove noise and smooth rough edges. For 8-bit greyscale image, thresholding and background removal are processing steps to improve image quality for vectorisation. For color images, they are often classified to separate the colors so each color can be vectorised into a separate vector layer.
Defining regions of interest (ROI) for vectorisation or image cropping is another often used preprocessing step to limit the processing only in the areas interested. It is important to allow the use of polygons and group of polygons to include cases such as islands, holes, rings and other shapes.
Image mosaic or stitching is normally done when a source map is larger than the scanner can handle. In this case, the map is scanned into sub-sections and then merged into a whole image for raster to vector conversion. This is often done as a post-processing step by merging the vector data sets after each section is vectorised. Merging vector data instead of raster image certain has its advantages, because vector data takes much less computer memory and can be processed faster while image stitching can create huge size images that are beyond the processing capability of a regular PC.
Shows that closed polygons are created from vectorised line segments.
The line tracing process extracts two types of lines: center line and boundary line. The center line method tracks the center pixel within a raster line and follow to the line until it reaches an intersection or the end of the line. The boundary line method tracks the boundary pixels of a color region to get closed polygons.
Although there have been many methods developed for line tracing, they can be divided into two groups: line thinning and line following. The line thinning method is more of a global approach, which iterates through the entire image in multiple passes and eliminates boundary pixels during each iteration until only the skeleton pixels are left. The line following method uses computer intelligence to analyze line shapes, thickness and intersections to follow the line centers. This method is frequently employed in semi-automatic interactive tracing while line thinning based methods is used for fully automatic conversion of complex images.
After lines are extracted, they are labeled with line attributes or elevations if contours. Closed polygons can be generated from line segments to create the topology. Control points are defined and applied to geo-reference the vector data to a projection system.
One common use of labeled contour lines is creation of 3D DEM (Digital Terrain Model) and other 3D data models. We will be seeing more and more use of 3D display in the next couple of years in GIS and computer mapping applications. The use of 3D visualisation gets us one step closer to the 3D world we live in but it puts more demand on computer software and hardware. Many people think today’s computer technology is far more powerful than we need, they are right if word processing is what they do everyday. They will be surprised how much more computing power is badly needed when 3D digital terrain model is used in real time and how much worse it can get when high resolution satellite imagery are draped onto the surface of the digital terrain model. We are quite sure that faster CPU, bigger memory and better quality display will not be wasted in GIS and computer mapping applications.
Choosing the Right Conversion Tool
Several raster to vector conversion software packages are commercially available for different applications, such as engineering drawing conversion, map digitising and GIS data capture. The R2V software developed by Able Software Corp. (www.ablesw.com) in 1993 has a focus on vectorisation of scanned maps and GIS data creation.
Below are few questions one should ask when selecting the right tool for the task:
- Does it support different image types, such as 1-bit black/white, greyscale and 24-bit RGB color? This is quite important for people whose source images are in color. Treating color images as black and white or greyscale apparently loses all color information and a significant amount of editing may be needed to separate colors by hand.
- Is it designed for maps or engineering drawings? In practice, the handling of map data and engineering data are quite different although they both are vectors based. If a package is designed for CAD drawings, the algorithms normally works well for straight lines and regular geometric shapes and will not be efficient for curving lines, polygons and topology between polygons. Geo-referencing is another crucial factor for maps and GIS database while it is normally not a concern for CAD applications.
- Does it support the native format for your application? It’s unfortunate that most vector file formats used today are different and data exchange between two formats can easily result some data loss. One format may be excellent for CAD data transfer, but very limited if you need to get data into a GIS or mapping database. When creating vector data, it is always better to use the native format the target system supports.
- Image processing functions The quality of raster to vector conversion depends largely on the quality of the source image that is affected by many factors, including scanner, cleanness and age of the source map, scanning resolution, color or black/white, and others. Without necessary image processing functions, such as remove background for old maps with blue background, color separation for color maps, define polygon-based region of interest (ROI), image rubber sheeting to correct distortion, the usefulness of the final vector product may be quite limited.
Shows that boundary lines are traced directly from a classified SPOT image. Different color regions are traced and put into separate map layers.
Because of the complexity, automated raster to vector conversion has attracted a significant amount of research focus in the past decades among GIS and image processing communities. Although commercial products have been developed and used in production type applications for large scale map digitising and GIS data capture projects, there is still room for improvements and demand for new algorithms and technologies, for example, color image processing and color separation, text recognition (OCR), use of satellite imagery to create vector map layers and others.
When 24-bit true color is used to scan color maps or drawings, each pixel has 3 color components (red, green and blue). Each component is recorded as an 8-bit integer number with the value range of 0 – 255. Roughly, a 24-bit color image can have up to 16.7 million different colors. Classifying the millions of colors into a small number of color groups becomes a challenge, especially when some color groups have only small number of pixels and the source image quality is not perfect. Clustering based color classification and ration based methods have been developed to solve this problem but in many cases, more robust methods are needed to achieve more satisfactory color separation result.
Text recognition (OCR) is another challenge faced by developers and researchers. To reliably recognize text labels in maps, the first step is to separate them from lines, also known as the text segmentation step. Once separated, text recognition engines are applied to identify the text and convert them to computer readable ASCII code, or unicode for other languages, such as Chinese and Japanese. Conventional text recognition (OCR) technologies have not worked well for recognizing text in maps and drawings, largely due to the variety of fonts, sizes and orientations used. International languages add more difficulty to the problem.
When high resolution satellite imagery become more affordable and easily accessible, we will be seeing more use of them to create GIS data layers and update existing map data. To automate the process from raw satellite imagery to finished vector data layers, new methods and products will be developed to recognize and map natural objects, such as roads, building roof tops, trees, vegetation, water and so on. Not only lines and polygons will be generated from the images, but also important attribute and layer information associated with the graphical objects.
- Y. Wu, “Raster, Vector, and Automated Raster-to-Vector Conversion”, in “Moving Theory into Practice: Digital Imaging for Libraries and Archives”, book eds. by Anne R. Kinney and Oya Y. Rieger, 2000, RLG, Cornell Univ. Library
- Y. Wu, “R2V Conversion: Why and How?”, GeoInformatics, No. 6, Volume 3, Sept. 2000, pp. 28-31
- L.R. Poos and Y. Wu, “Digitizing History: GIS and Historical Research”, GIS World, July 1995, pp. 48-51
- J.R. Parker, “Algorithms for Image Processing and Computer Vision”, 1997, John Wiley & Sons.