Sketch recognition

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

The Convolutional Neural Network Model Based on an Evolutionary Approach For Interactive Picture Book

Abstract: Creating interactive picture books based on human “Kansei” is one of the most interesting and difficult issues in the artificial intelligence field. We have proposed a novel interactive picture book based on Pictgent (Picture Information Shared Conversation Agent) and CASOOK (Creative Animating Sketchbook). Since our system accepts human sketches instead of natural languages, a high degree of sketch recognition accuracy is required. Recently, convolutional neural networks (CNNs) have been applied to various image- recognition tasks successfully. We have also adopted a CNN model for the sketch recognition of the proposed interactive picture book. However, it takes a considerable effort to tune the hyperparameters of a CNN. In this paper, we propose a novel parameter tuning method for CNNs using an evolutionary approach. The effectiveness of the proposed method is confirmed by a computer simulation that uses, as an example, a scribble-object recognition problem for the interactive picture book.

Contextual focus for improved recognition of hand-filled forms

Abstract: The advances in Optical Character Recognition (OCR) technology over the past decade have enabled the development of many automatic document-processing systems capable of 99% correct recognition on printed text However, similar advances in Cursive Script Recognition (CSR) technology have not been forthcoming due, principally, to the vast variability of human handwriting This paper investigates a method by which the more reliable OCR technology can be used to improve the CSR performance in a form processing application A novel method is proposed to link handwriting data to contextual cue words that have been automatically obtained from an OCR process This information is then used to select appropriate 'focused' lexicons to achieve better CSR results The method was tested on 30 forms that were filled by 10 different writers The experimental results together with a comparison to the base line recognition performance are presented

A review on machine learning algorithms on human action recognition

Abstract: Human action recognition is a vital field of computer vision research Its applications incorporate observation frameworks, patient monitoring frameworks, and an assortment of frameworks that include interactions between persons and electronic gadgets, for example, human-computer interfaces The vast majority of these applications require an automated recognition of abnormal or anomalistic action states, made out of various straightforward (or nuclear) actions of persons This study gives an overview of different best in class research papers on human movement recognition Open datasets intended for the assessment of the recognition procedures are also discussed in this paper too, for comparing results of several methodologies on this datasets We examine both the approaches produced for basic human actions and those for abnormal action states These methodologies are taxonomically classified based on looking at the points of interest and constraints of every methodology Space-time volume approaches and sequential methodologies that represent actions and perceive such action sets straightforwardly from images are discussed Next, hierarchical recognition approaches for abnormal action states are introduced and looked at Statistics based methodologies, syntactic methodologies, and description based methodologies for hierarchical recognition is examined in the paper

Recognizing interspersed sketches quickly

Abstract: Sketch recognition is the automated recognition of hand-drawn diagrams. When allowing users to sketch as they would naturally, users may draw shapes in an interspersed manner, starting a second shape before finishing the first. In order to provide freedom to draw interspersed shapes, an exponential combination of subshapes must be considered. Because of this, most sketch recognition systems either choose not to handle interspersing, or handle only a limited pre-defined amount of interspersing. Our goal is to eliminate such interspersing drawing constraints from the sketcher. This paper presents a high-level recognition algorithm that, while still exponential, allows for complete interspersing freedom, running in near real-time through early effective sub-tree pruning. At the core of the algorithm is an indexing technique that takes advantage of geometric sketch recognition techniques to index each shape for efficient access and fast pruning during recognition. We have stresstested our algorithm to show that the system recognizes shapes in less than a second even with over a hundred candidate subshapes on screen.