Sketch recognition

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

Multi-domain sketch understanding

Abstract: People use sketches to express and record their ideas in many domains, including mechanical engineering, software design, and information architecture. In recent years there has been an increasing interest in sketch-based user interfaces, but the problem of robust free-sketch recognition remains largely unsolved. Current computer sketch recognition systems are difficult to construct, and either are fragile or accomplish robustness by severely limiting the designer's drawing freedom. This work explores the challenges of multi-domain sketch recognition. We present a general framework and implemented system, called SketchREAD , for diagrammatic sketch recognition. Our system can be applied to a variety of domains by providing structural descriptions of the shapes in the domain. Robustness to the ambiguity and uncertainty inherent in complex, freely-drawn sketches is achieved through the use of context. Our approach uses context to guide the search for possible interpretations and uses a novel form of dynamically constructed Bayesian networks to evaluate these interpretations. This process allows the system to recover from low-level recognition errors (e.g., a line misclassified as an arc) that would otherwise result in domain level recognition errors. We evaluated SketchREAD on real sketches in two domains—family trees and circuit diagrams—and found that in both domains the use of context to reclassify low-level shapes significantly reduced recognition error over a baseline system that did not reinterpret low-level classifications. We discuss remaining challenges for multi-domain sketch recognition revealed by our evaluation. Finally, we explore the system's potential role in sketch-based user interfaces from a human computer interaction perspective. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

On the Recognition of Information With a Digital Computer

Abstract: There exists a vast discrepancy in the power of discrimination exercised by a digital computer and in that of a human being. The recognition of information or data patterns is a simple task for the least experienced human clerk. Most people possess sufficiently sophisticated recognition capabilities so that a variation in the pattern of the data under scrutiny will not cause undue difficulty in the discrimination process. The recognition powers of a digital computer, however, are best demonstrated in an elementary table lookup operation, wherein the subject information is required to match exactly with a portion of the master list in order to be “recognized”. Machine recognition of data which is allowed to vary from the predetermined digital pattern is a vastly more complex problem. Since digital computers are inherently devices which are capable of only YES or NO answers, all MAYBE or PERHAPS responses are obtained only through painstaking effort. If the variations in the subject data are allowed a reasonable range in both position and type, there is no complete solution of the recognition problem available with present techniques and equipments. This paper will outline the general recognition problem in terms of a set of definitions and a mathematical model. I believe that a useful formulation of the problem, and a comprehension of the difficulties involved in discrimination, are prerequisites to any effort at obtaining a complete solution to the problem of data recognition.

Scene Text Recognition Using Structure-Guided Character Detection and Linguistic Knowledge

Abstract: Scene text recognition has inspired great interests from the computer vision community in recent years. In this paper, we propose a novel scene text-recognition method integrating structure-guided character detection and linguistic knowledge. We use part-based tree structure to model each category of characters so as to detect and recognize characters simultaneously. Since the character models make use of both the local appearance and global structure informations, the detection results are more reliable. For word recognition, we combine the detection scores and language model into the posterior probability of character sequence from the Bayesian decision view. The final word-recognition result is obtained by maximizing the character sequence posterior probability via Viterbi algorithm. Experimental results on a range of challenging public data sets (ICDAR 2003, ICDAR 2011, SVT) demonstrate that the proposed method achieves state-of-the-art performance both for character detection and word recognition.

Sketch recognition algorithms for comparing complex and unpredictable shapes

Abstract: In an introductory Engineering course with an annual enrollment of over 1000 students, a professor has little option but to rely on multiple choice exams for midterms and finals. Furthermore, the teaching assistants are too overloaded to give detailed feedback on submitted homework assignments. We introduce Mechanix, a computer-assisted tutoring system for engineering students. Mechanix uses recognition of freehand sketches to provide instant, detailed, and formative feedback as the student progresses through each homework assignment, quiz, or exam. Free sketch recognition techniques allow students to solve free-body diagram and static truss problems as if they were using a pen and paper. The same recognition algorithms enable professors to add new unique problems simply by sketching out the correct answer. Mechanix is able to ease the burden of grading so that instructors can assign more free response questions, which provide a better measure of student progress than multiple choice questions do.

Speech Recognition with Hidden Markov Model: A Review

Abstract: The concept of Recognition one phase of Speech Recognition Process using Hidden Markov Model has been discussed in this paper. Preprocessing, Feature Extraction and Recognition three steps and Hidden Markov Model (used in recognition phase) are used to complete Automatic Speech Recognition System. Today's life human is able to interact with computer hardware and related machines in their own language. Research followers are trying to develop a perfect ASR system because we have all these advancements in ASR and research in digital signal processing but computer machines are unable to match the performance of their human utterances in terms of accuracy of matching and speed of response. In case of speech recognition the research followers are mainly using three different approaches namely Acoustic phonetic approach, Knowledge based approach and Pattern recognition approach. This paper's study is based on pattern recognition approach and the third phase of speech recognition process 'Recognition' and Hidden Markov Model is studied in detail.