Radar data processing and analysis

Digitized four-channel radar images corresponding to particular areas from the Phoenix and Huntington test sites were generated in conjunction with prior experiments performed to collect X- and L-band synthetic aperture radar imagery of these two areas. The methods for generating this imagery are documented. A secondary objective was the investigation of digital processing techniques for extraction of information from the multiband radar image data. Following the digitization, the remaining resources permitted a preliminary machine analysis to be performed on portions of the radar image data. The results, although necessarily limited, are reported

Novel geometric features for off-line writer identification

Writer identification is an important field in forensic document examination. Typically, a writer identification system consists of two main steps: feature extraction and matching and the performance depends significantly on the feature extraction step. In this paper, we propose a set of novel geometrical features that are able to characterize different writers. These features include direction, curvature, and tortuosity. We also propose an improvement of the edge-based directional and chain code-based features. The proposed methods are applicable to Arabic and English handwriting. We have also studied several methods for computing the distance between feature vectors when comparing two writers. Evaluation of the methods is performed using both the IAM handwriting database and the QUWI database for each individual feature reaching Top1 identification rates of 82 and 87 % in those two datasets, respectively. The accuracies achieved by Kernel Discriminant Analysis (KDA) are significantly higher than those observed before feature-level writer identification was implemented. The results demonstrate the effectiveness of the improved versions of both chain-code features and edge-based directional features

Handwritten Digit Recognition by Fourier-Packet Descriptors

Any statistical pattern recognition system includes a feature extraction component. For character patterns, several feature families have been tested, such as the Fourier-Wavelet Descriptors. We are proposing here a generalization of this family: the Fourier-Packet Descriptors. We have selected sets of these features and tested them on handwritten digits: the error rate was 1.55% with a polynomial classifier for a 70 features set and 1.97% with a discriminative learning quadratic discriminant function for a 40 features set

Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation

Single Slice Grouping Mechanism for Recognition of Cursive Handwritten Courtesy Amounts of Malaysian Bank Cheques

Mechanism to group single slice for recognition involves the process of cutting vertically across an image slice by slice, group every slice at a certain width and tested for recognition using a trained Neural network. The image contains cursive handwritten courtesy Amounts of Malaysian bank cheques. A three layer neural Network architecture with the new error function of Backpropagation learning algorithm is used. This approach yields good recognition results with faster convergence rates

Handwritten number recognition

The main focus of this research is to automate medical form processing. An important step in this process is separating handwriting from printed text characters. We developed a filtering technique that extracts handwritten text from the printed text in the form. Once the handwritten text is segregated, each line of the segregated text is identified. The identification step is followed by character segmentation. Statistical analysis is performed on the gaps between the characters in each line. This results in a binormal curve clearly depicting two regions indicating if the gap represents the spacing between characters within a word or between two words. Furthermore, an algorithm is employed for number recognition. We use different feature extraction algorithms and generate a high dimension feature vector. The algorithm is trained by giving training samples; a rule is generated to classify an input. A rule database is created in order classify the characters given during testing phase. By this method, there is no need to correlate the observed number with the pre-stored characteristics of numbers, instead we test the given number whether it satisfies the appropriate rule

Handwritten Digit Recognition and Classification Using Machine Learning

In this paper, multiple learning techniques based on Optical character recognition (OCR) for the handwritten digit recognition are examined, and a new accuracy level for recognition of the MNIST dataset is reported. The proposed framework involves three primary parts, image pre-processing, feature extraction and classification. This study strives to improve the recognition accuracy by more than 99% in handwritten digit recognition. As will be seen, pre-processing and feature extraction play crucial roles in this experiment to reach the highest accuracy