Sketch recognition

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

Supporting Chinese Character Educational Interfaces with Richer Assessment Feedback through Sketch Recognition

Abstract: Students of Chinese as a Second Language (CSL) with primarily English fluency often struggle with the language's complex character set. Conventional classroom pedagogy and relevant educational applications have focused on providing valuable assessment feedback to address their challenges, but rely on direct instructor observation and provide constrained assessment, respectively. We propose improved sketch recognition techniques to better support Chinese character educational interfaces' realtime assessment of novice CSL students' character writing. Based on successful assessment feedback approaches from existing educational resources, we developed techniques for supporting richer automated assessment, so that students may be better informed of their writing performance outside the classroom. From our evaluations, our techniques achieved recognition rates of 91% and 85% on expert and novice Chinese character handwriting data, respectively, greater than 90% recognition rate on written technique mistakes, and 80.4% f-measure on distinguishing between expert and novice handwriting samples, without sacrificing students' natural writing input of Chinese characters.

Iterative samples labeling for sketch recognition

Abstract: Sketch recognition is an important issue in human-computer interaction, especially in sketch-based interface. To provide a scalable and flexible tool for user-centered sketch recognition, this paper proposes an iterative sketch collection annotation method for classifier-training by interleaving online metric learning, semi-supervised clustering and user intervention. It can discover the categories of the collections iteratively by combing online metric learning with semi-supervised clustering, and put the user intervention into the loop of each iteration. The features of our methods lie in three aspects. Firstly, the unlabeled collections are annotated with less effort in a group by group form. Secondly, users can annotate the collections flexibly and freely to define the sketch recognition personally for different applications. Finally, the scalable collection can be annotated efficiently by combining the dynamically processing and online learning. The extensive experimental results prove the effectiveness of our proposed method.

Recognising hand-written Japanese sentences

Abstract: This paper makes a case for handwriting recognition compared to other input methods for communication with machines. A comparison is made with voice recognition and keyboard input systems for both western languages and for Japanese. Both single word recognition and whole sentence recognition are considered. A case is made for handwriting recognition for a language with a large character set and many homonyms, such as the Japanese language. For such a language, a fundamental problem exists for both keyboard input and for voice recognition. Both these systems need to convert a phonetic representation into Kanji, and this requires extensive knowledge of the meaning of the text if it is to be automatic. AI research has yet to deliver fast, competent text understanding systems. Consequently, both voice and keyboard input methods need to present the user with alternative choices during recognition, and this makes these methods slow and unnatural. A system is described here which is designed for accurate, fast sentence recognition of both western scripts and Japanese. The system is designed for whole sentence recognition, with the user allowed to write in a natural way. There is considerable flexibility allowed in terms of size and shape of the writing. The distinguishing characteristic of the system, is the use of a unified recognition technique applied to character, word and sentence recognition. This technique is an adaptation of chart parsing, used extensively in natural language processing in AI. Here the technique has been developed to allow weighted multiple hypotheses during recognition. This is important for a system that allows the user to write naturally. This approach to sentence recognition, allows mistakes made during low level processing to be corrected at higher levels. Knowledge of the vocabulary and allowable sentence structures are incorporated in the system in a unified way. A useful additional result of this approach, is the ability to produce a syntactic parse of the sentence recognised. Provisional results are presented for recognition of Japanese Hiragana characters and for English capital letters. The users were given considerable freedom on the style of writing used. The results show recognition rates of over 80% at present, for a variety of users. Improvements in this performance are anticipated when lexical and syntactic modules are added. Further improvements are anticipated by incorporating learning into the system, so that the knowledge base will be tuned for each user. >

A Method for Emotional Model Construction Oriented to Industrial Designer Sketch Recognition

Abstract: Learning from methods of sketch research in computer image retrieval area at present, the aim of this paperis to research sketch based on industrial designers' kansei image to improve the efficiency and accuracy of the sketch design. A feature extraction method is proposedfor industrial design sketch according to the influence of product eigenstructure lines on product form and stream line form, conducts thorough statistical analysis of kansei image contained in the sketch feature samples combined with the Fuzzy-AHP method, and builds an emotional model for industrial designers sketch recognition, to guide the designer to express feelings adequately in the process of drafting.