Sketch recognition

About: Sketch recognition is a research topic. Over the lifetime, 1611 publications have been published within this topic receiving 40284 citations.

Sketching by perceptual grouping

Abstract: Sketch is used for rendering the visual world since prehistoric times, and has become ubiquitous nowadays with the increasing availability of touchscreens on portable devices. However, how to automatically map images to sketches, a problem that has profound implications on applications such as sketch-based image retrieval, still remains open. In this paper, we propose a novel method that draws a sketch automatically from a single natural image. Sketch extraction is posed within an unified contour grouping framework, where perceptual grouping is first used to form contour segment groups, followed by a group-based contour simplification method that generate the final sketches. In our experiment, for the first time we pose sketch evaluation as a sketch-based object recognition problem and the results validate the effectiveness of our system over the state-of-the-arts alternatives.

Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search.

Abstract: In recent years, image databases are growing at exponential rates, making their management, indexing, and retrieval, very challenging. Typical image retrieval systems rely on sample images as queries. However, in the absence of sample query images, hand-drawn sketches are also used. The recent adoption of touch screen input devices makes it very convenient to quickly draw shaded sketches of objects to be used for querying image databases. This paper presents a mechanism to provide access to visual information based on users’ hand-drawn partially colored sketches using touch screen devices. A key challenge for sketch-based image retrieval systems is to cope with the inherent ambiguity in sketches due to the lack of colors, textures, shading, and drawing imperfections. To cope with these issues, we propose to fine-tune a deep convolutional neural network (CNN) using augmented dataset to extract features from partially colored hand-drawn sketches for query specification in a sketch-based image retrieval framework. The large augmented dataset contains natural images, edge maps, hand-drawn sketches, de-colorized, and de-texturized images which allow CNN to effectively model visual contents presented to it in a variety of forms. The deep features extracted from CNN allow retrieval of images using both sketches and full color images as queries. We also evaluated the role of partial coloring or shading in sketches to improve the retrieval performance. The proposed method is tested on two large datasets for sketch recognition and sketch-based image retrieval and achieved better classification and retrieval performance than many existing methods.

AraPen: An Arabic Online Handwriting Recognition System

Abstract: In this paper we present AraPen, a trainable system we developed to recognize Arabic online handwriting. The system is based on mathematical matching techniques and our testing results show high recognition rate for non-cursive character recognition, and are promising for cursive recognition. The low memory and CPU requirements enable the system to run on low-end devices interactively.

Maestoso: an intelligent educational sketching tool for learning music theory

Abstract: Learning music theory not only has practical benefits for musicians to write, perform, understand, and express music better, but also for both non-musicians to improve critical thinking, math analytical skills, and music appreciation. However, current external tools applicable for learning music theory through writing when human instruction is unavailable are either limited in feedback, lacking a written modality, or assuming already strong familiarity of music theory concepts. In this paper, we describe Maestoso, an educational tool for novice learners to learn music theory through sketching practice of quizzed music structures. Maestoso first automatically recognizes students' sketched input of quizzed concepts, then relies on existing sketch and gesture recognition techniques to automatically recognize the input, and finally generates instructor-emulated feedback. From our evaluations, we demonstrate that Maestoso performs reasonably well on recognizing music structure elements and that novice students can comfortably grasp introductory music theory in a single session.

An adaptive approach to offline handwritten word recognition

Abstract: An adaptive handwritten word recognition method is presented. A recursive architecture based on interaction between flexible character classification and deductive decision making is developed. The recognition process starts from the initial coarse level using a minimum number of features, then increases the discrimination power by adding other features adaptively and recursively until the result is accepted by the decision maker. For the computational aspect of a feasible solution, a unified decision metric, recognition confidence; is derived from two measurements: pattern confidence, evaluation of absolute confidence using shape features, and lexical confidence, evaluation of the relative string dissimilarity in the lexicon. Practical implementation and experimental results in reading the handwritten words of the address components of US mail pieces are provided. Up to a 4 percent improvement in recognition performance is achieved compared to a nonadaptive method. The experimental result shows that the proposed method has advantages in producing valid answers using the same number of features as conventional methods.