Recognizing Elementary Elements in Chemical Diagram Sketches

Organic Chemistry is a challenging subject that requires dedicated practice to learn the meticulous rules composing the subject, otherwise a student risks failure. Current software to teach chemical structures contains drag-and-drop components and fails to provide students with true understanding of Organic Chemistry concepts. My solution is to integrate a sketch recognition interface that can learn to recognize components of various, user-sketched chemical structures with a back-propagation neural network that can be trained to translate the components of the chemical structure to determine correctness. The accuracy of the program will be rigorously tested to determine correctness in interpreting chemical structures

Recognizing Elementary Elements in Chemical Diagram Sketches

Organic Chemistry is a challenging subject that requires dedicated practice to learn the meticulous rules composing the subject, otherwise a student risks failure. Current software to teach chemical structures contains drag-and-drop components and fails to provide students with true understanding of Organic Chemistry concepts. My solution is to integrate a sketch recognition interface that can learn to recognize components of various, user-sketched chemical structures with a back-propagation neural network that can be trained to translate the components of the chemical structure to determine correctness. The accuracy of the program will be rigorously tested to determine correctness in interpreting chemical structures

An evaluation of user experience with a sketch-based 3D modeling system

Abstract With the availability of pen-enabled digital hardware, sketch-based 3D modeling is becoming an increasingly attractive alternative to traditional methods in many design environments. To date, a variety of methodologies and implemented systems have been proposed that all seek to make sketching the primary interaction method for 3D geometric modeling. While many of these methods are promising, a general lack of end user evaluations makes it difficult to assess and improve upon these methods. Based on our ongoing work, we present the usage and a user evaluation of a sketch-based 3D modeling tool we have been developing for industrial styling design. The study investigates the usability of our techniques in the hands of non-experts by gauging (1) the speed with which users can comprehend and adopt to constituent modeling steps, and (2) how effectively users can utilize the newly learned skills to design 3D models. Our observations and users' feedback indicate that overall users could learn the investigated techniques relatively easily and put them in use immediately. However, users pointed out several usability and technical issues such as difficulty in mode selection and lack of sophisticated surface modeling tools as some of the key limitations of the current system. We believe the lessons learned from this study can be used in the development of more powerful and satisfying sketch-based modeling tools in the future.

Techniques for creating ground-truthed sketch corpora

The problem of recognizing handwritten mathematics notation has been studied for over forty years with little practical success. The poor performance of math recognition systems is due, at least in part, to a lack of realistic data for use in training recognition systems and evaluating their accuracy. In fields for which such data is available, such as face and voice recognition, the data, along with objectively-evaluated recognition contests, has contributed to the rapid advancement of the state of the art. This thesis proposes a method for constructing data corpora not only for hand- written math recognition, but for sketch recognition in general. The method consists of automatically generating template expressions, transcribing these expressions by hand, and automatically labelling them with ground-truth. This approach is motivated by practical considerations and is shown to be more extensible and objective than other potential methods. We introduce a grammar-based approach for the template generation task. In this approach, random derivations in a context-free grammar are controlled so as to generate math expressions for transcription. The generation process may be controlled in terms of expression size and distribution over mathematical semantics. Finally, we present a novel ground-truthing method based on matching terminal symbols in grammar derivations to recognized symbols. The matching is produced by a best-first search through symbol recognition results. Experiments show that this method is highly accurate but rejects many of its inputs