Context: Many scientific and technical literature documents contain MSs and MEs that are
more challenging to be recognized by computers than plain text. The recognition of HMSE …

Engineering drawings are commonly used in different industries such as Oil and Gas,
construction, and other types of engineering. Digitising these drawings is becoming …

We address the problem of offline handwritten diagram recognition. Recently, it has been
shown that diagram symbols can be directly recognized with deep learning object detectors …

Online handwritten diagram recognition (OHDR) has attracted considerable attention for its
potential applications in many areas, but it is a challenging task due to the complex 2D …

In this work we investigate the use of deep neural networks for object detection in floor plan
images. Object detection is important for understanding floor plans and is a preliminary step …

Process models play an important role in various software engineering contexts. Among
others, they are used to capture business-related requirements and provide the basis for the …

Offline hand-drawn diagram recognition is concerned with digitizing diagrams sketched on
paper or whiteboard to enable further editing. Some existing models can identify the …

We describe a technique for transcript alignment in early printed books by using deep
models in combination with dynamic programming algorithms. Two object detection models …

Detection of small objects in natural scene images is a complicated problem due to the blur
and depth found in the images. Detecting house numbers from the natural scene images in …

We propose Arrow R-CNN for recognizing the symbols and structure of offline handwritten
flowcharts. Arrow R-CNN extends the Faster R-CNN object detection system with an arrow …