Sketch recognition

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

Multi-lingual text recognition from video frames

Abstract: Text recognition from video frames is a challenging task due to low resolution, blur, complex and coloured backgrounds, noise, to mention a few. Consequently, the traditional ways of text recognition from scanned documents having simple backgrounds fails when applied to video text. Although there are various techniques available for text recognition from handwritten and printed documents with simple backgrounds, text recognition from video frames has not been comprehensively investigated, especially for multi-lingual videos. In this paper, we present a technique for multi-lingual video text recognition which involves script identification in the first stage, followed by word and character recognition, and finally the results are refined using a post-processing technique. Considering the inherent problems in videos, a Spatial Pyramid Matching (SPM) based technique, using patch-based SIFT descriptors and SVM classifier, is employed for script identification. In the next stage, a Hidden Markov Model (HMM) based approach is used for word and character recognition, which utilizes the context information. Finally, a lexicon-based post-processing technique is applied to verify and refine the word recognition results. The proposed method was tested on a dataset comprising of 4800 words from three different scripts, namely, Roman (English), Hindi and Bengali. The script identification results obtained are encouraging. The word and character recognition results are also encouraging considering the complexity and problems associated with video text processing.

Recognition, machine “recognition”, and statistical approaches

Abstract: Publisher Summary Although perception and recognition, and the logic of classification and statistical methodology have each been studied for decades, automatic pattern recognition is a new field. It is the recent emergence of computer related technology and needs that have led to serious attempts to synthesize various aspects of pattern recognition into machine recognition systems. In automatic pattern recognition, there is confusion between the goals of mechanical simulation of human perception and recognition, and machine systems to automate certain data-handling processes by performing a few pattern classification tasks. While in some situations these goals are related, they are nevertheless distinct. It has been long recognized that pattern recognition is an important aspect of almost all activities to which the appellation intelligent can be added confidently. This chapter discusses the relationship between recognition and classification, and between theory and application of statistical classification. Compound decision theory is the latest step in the evolution of the most general model in which to imbed statistical classification problems arising in recognition system design.

Facial Self Similarity for Sketch to Photo Matching

Abstract: Automatic recognition of suspects from forensic sketches is of considerable interest to the law enforcement agencies. However, this task is complex due to the heterogenous nature of face sketches and photographs. To address this challenge, previous approaches generally learn a transformation of a sketch to photo or a photo to sketch at the image or feature level in order to reduce the modality gap. Such a transformation may be indeterministic and if learned from training data, is likely to over-fit the sketch artist's drawing technique. Instead, we formulate the problem in the context of matching local self similarities which are independently computed from a face sketch and a photo. The proposed Facial Self Similarity (FSS) descriptor is obtained by correlation of a small face patch with its local neighborhood. Thus, our approach avoids the need of a modality transformation, while implicitly reducing the inter-modality gap. The proposed FSS descriptor is evaluated on the CUHK Face Sketch database using sketch-photo pairs of 311 subjects. The FSS descriptor demonstrates high recognition accuracy of 99.53% and outperforms current techniques. We also evaluate the robustness of the descriptor to anomalies such as matching sketches to blurred photographs.

A preliminary study of a hybrid user interface for augmented reality applications

Abstract: Augmented Reality (AR) applications are nowadays largely diffused in many fields of use, especially for entertainment, and the market of AR applications for mobile devices grows faster and faster. Moreover, new and innovative hardware for human-computer interaction has been deployed, such as the Leap Motion Controller. This paper presents some preliminary results in the design and development of a hybrid interface for hand-free augmented reality applications. The paper introduces a framework to interact with AR applications through a speech and gesture recognition-based interface. A Leap Motion Controller is mounted on top of AR glasses and a speech recognition module completes the system. Results have shown that, using the speech or the gesture recognition modules singularly, the robustness of the user interface is strongly dependent on environmental conditions. On the other hand, a combined usage of both modules can provide a more robust input.

Sketching: A Computational Paradigm for Personalized Searching

Abstract: Work on computer-aided design in general and sketch recognition in particular has been jointly sponsored by the Division of Computer Research of the National Science Foundation (Grant Number DCR74-20974-A01, Machine Recognition and Inference Making in Computer Aids to Design) and the Office of Naval Research (Contract Number N0014-67-A-0204-0074, Context Definition through Idiosyncratic Systems).