295 research outputs found
Evaluation of Pattern Classifiers for Fingerprint and OCR Applications
(Also cross-referenced as CAR-TR-691)
In this paper we evaluate the classification accuracy of four
statistical and three neural network classifiers for two image based
pattern classification problems. These are fingerprint classification and
optical character recognition (OCR) for isolated handprinted digits. The
evaluation results reported here should be useful for designers of
practical systems for these two important commercial applications. For the
OCR problem, the Karhunen-Loeve (K-L) transform of the images is used to
generate the inp ut feature set. Similarly for the fingerprint problem,
the K-L transform of the ridge directions is used to generate the input
feature set. The statistical classifiers used were Euclidean minimum
distance, quadratic minimum distance, normal, and knearest neighbor. The
neural network classifiers used were multilayer perceptron, radial basis
function, and probabilistic. The OCR data consisted of 7,480 digit images
for training and 23,140 digit images for testing. The fingerprint data
consisted of 9,000 trai ning and 2,000 testing images. In addition to
evaluation for accuracy, the multilayer perceptron and radial basis
function networks were evaluated for size and generalization capability.
For the evaluated datasets the best accuracy obtained for either pro blem
was provided by the probabilistic neural network, where the minimum
classification error was 2.5% for OCR and 7.2% for fingerprints
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Learning-based pattern classifiers, including deep networks, have shown
impressive performance in several application domains, ranging from computer
vision to cybersecurity. However, it has also been shown that adversarial input
perturbations carefully crafted either at training or at test time can easily
subvert their predictions. The vulnerability of machine learning to such wild
patterns (also referred to as adversarial examples), along with the design of
suitable countermeasures, have been investigated in the research field of
adversarial machine learning. In this work, we provide a thorough overview of
the evolution of this research area over the last ten years and beyond,
starting from pioneering, earlier work on the security of non-deep learning
algorithms up to more recent work aimed to understand the security properties
of deep learning algorithms, in the context of computer vision and
cybersecurity tasks. We report interesting connections between these
apparently-different lines of work, highlighting common misconceptions related
to the security evaluation of machine-learning algorithms. We review the main
threat models and attacks defined to this end, and discuss the main limitations
of current work, along with the corresponding future challenges towards the
design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201
Identification of Alphanumeric Pattern Using Android
The “Identification of Alphanumeric pattern using Android” is a smart phone apps using Android platform and combines the functionality of Optical Character Recognition and identification of alphanumeric pattern and after processing, data is stored in server. This paper present, to design an apps using the Android SDK that will enable the Identification of Alphanumeric pattern using optical character reader technique for the Android based smart phone application. Camera, captures the document image and then the OCR is convert that image in to text (Binarization of captured data) according to the Alphanumeric (alphabetic and numeric characters) database and data stored in server.
DOI: 10.17762/ijritcc2321-8169.160414
Image-based Automated Chemical Database Annotation with Ensemble of Machine-Vision Classifiers
This paper presents an image-based annotation strategy for automated annotation of chemical databases. The proposed strategy is based on the use of a machine vision-based classifier for extracting a 2D chemical structure diagram in research articles and converting them into standard chemical file formats, a virtual Chemical Expert" system for screening the converted structures based on the level of estimated conversion accuracy, and a fragment-based measure for calculation intermolecular similarity. In particular, in order to overcome limited accuracies of individual machine-vision classifier, inspired by ensemble methods in machine learning, it is attempted to use of the ensemble of machine-vision classifiers. For annotation, calculated chemical similarity between the converted structures and entries in a virtual small molecule database is used to establish the links. Annotation test to link 121 journal articles to entries in PubChem database demonstrates that ensemble approach increases the coverage of annotation, while keeping the annotation quality (e.g., recall and precision rates) comparable to using a single machine-vision classifier.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87266/4/Saitou55.pd
Parametric classification in domains of characters, numerals, punctuation, typefaces and image qualities
This thesis contributes to the Optical Font Recognition problem (OFR), by developing a classifier system to differentiate ten typefaces using a single English character ‘e’. First, features which need to be used in the classifier system are carefully selected after a thorough typographical study of global font features and previous related experiments. These features have been modeled by multivariate normal laws in order to use parameter estimation in learning. Then, the classifier system is built up on six independent schemes, each performing typeface classification using a different method. The results have shown a remarkable performance in the field of font recognition. Finally, the classifiers have been implemented on Lowercase characters, Uppercase characters, Digits, Punctuation and also on Degraded Images
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
A feature extraction method for Arabic Offline Handwritten Recognition System using Naïve Bayes classifier
Handwriting recognition in the Arabic language is considered one of the most challenging problems and the accuracies in recognizing still need more enhancements due to the Arabic character’s nature, cursive writing, style, and size of writing in contrast to working with other languages. In this paper, we propose a system for Arabic Offline Handwritten Character Recognition based on Naïve Bayes classifier (NB). Extraction features preceded by divided the image of character into three horizontal and vertical zones and 3x3 zones in one and two dimensions respectively, then classified by Naïve Bayes. The performance of the system proposes evaluated by using the benchmark CENPARMI database reached up to 97.05% accuracy rate. Experimental results confirm a high enhancement inaccuracy rate in comparison with other Arabic Optical Character Recognition systems
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