Sketch recognition

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

Indian sign language recognition

Abstract: Understanding human motions can be posed as a pattern recognition problem. Applications of pattern recognition in information processing problems are diverse ranging from Speech, Handwritten character recognition to medical research and astronomy. Humans express time-varying motion patterns (gestures), such as a wave, in order to convey a message to a recipient. If a computer can detect and distinguish these human motion patterns, the desired message can be reconstructed, and the computer can respond appropriately. This paper represents a framework for a human computer interface capable of recognizing gestures from the Indian sign language. The complexity of Indian sign language recognition system increases due to the involvement of both the hands and also the overlapping of the hands. Alphabets and numbers have been recognized successfully. This system can be extended for words and sentences Recognition is done with PCA (Principal Component analysis). This paper also proposes recognition with neural networks. Further it is proposed that number of finger tips and the distance of fingertips from the centroid of the hand can be used along with PCA for robustness and efficient results.

Sentence recognition using artificial neural networks

Abstract: The paper describes an application of artificial neural networks (ANN) for natural language text reasoning. The task of knowledge discovery in text from a database, represented with a database file consisting of sentences with similar meanings but different lexico-grammatical patterns, was solved with ANNs which recognize the meaning of the text using training files with limited dictionary. The paper features recognition algorithms of text meaning from a selected source using 3-layer ANNs. Tests of the new method have also been described.

Memetic approach for matching sketches with digital face images

Abstract: One of the important cues in solving crimes and apprehending criminals is matching sketches with digital f ace images. This paper presents an automated algorithm that ext racts discriminating information from local regions of both sketches and digital face images. Structural information along with the minute details present in local facial regions are encod ed using multi-scale circular Weber’s Local descriptor. Further, an evolutionary memetic optimization is proposed to assign op timal weights to every local facial region to boost the identificat ion performance. Since, forensic sketches or digital face imag es can be of poor quality, a pre-processing technique is used to enhance the quality of images and improve the identificationperformance. Comprehensive experimental evaluation on diffe r nt sketch databases show that the proposed algorithm yields be tt r identification performance compared to existing algorithms and two commercial face recognition systems.

Towards machine learning based design pattern recognition

Abstract: Software design patterns are abstract descriptions of best practice solutions for recurring design problems. The information about which design pattern is implemented where in a software design is very helpful and important for software maintenance and evolution. This information is usually lost due to poor, obsolete or lack of documentation, which raises the importance of automatic recognition techniques. However, their vague and abstract nature allows them to be implemented in various ways, which gives them resistance to be automatically and accurately recognized. This paper presents the first recognition approach to be solely based on machine learning methods. We build a training dataset by using several existing recognition tools and we use feature selection methods to select the input feature vectors. Artificial neural networks are then trained to perform the whole recognition process. Our approach is evaluated by conducting an experiment to recognize six design patterns in an open source application.