2,012 research outputs found
NeuroWrite: Predictive Handwritten Digit Classification using Deep Neural Networks
The rapid evolution of deep neural networks has revolutionized the field of
machine learning, enabling remarkable advancements in various domains. In this
article, we introduce NeuroWrite, a unique method for predicting the
categorization of handwritten digits using deep neural networks. Our model
exhibits outstanding accuracy in identifying and categorising handwritten
digits by utilising the strength of convolutional neural networks (CNNs) and
recurrent neural networks (RNNs).In this article, we give a thorough
examination of the data preparation methods, network design, and training
methods used in NeuroWrite. By implementing state-of-the-art techniques, we
showcase how NeuroWrite can achieve high classification accuracy and robust
generalization on handwritten digit datasets, such as MNIST. Furthermore, we
explore the model's potential for real-world applications, including digit
recognition in digitized documents, signature verification, and automated
postal code recognition. NeuroWrite is a useful tool for computer vision and
pattern recognition because of its performance and adaptability.The
architecture, training procedure, and evaluation metrics of NeuroWrite are
covered in detail in this study, illustrating how it can improve a number of
applications that call for handwritten digit classification. The outcomes show
that NeuroWrite is a promising method for raising the bar for deep neural
network-based handwritten digit recognition.Comment: 6 pages, 10 figure
Design of CNN architecture for Hindi Characters
Handwritten character recognition is a challenging problem which received attention because of its potential benefits in real-life applications. It automates manual paper work, thus saving both time and money, but due to low recognition accuracy it is not yet practically possible. This work achieves higher recognition rates for handwritten isolated characters using Deep learning based Convolutional neural network (CNN). The architecture of these networks is complex and plays important role in success of character recognizer, thus this work experiments on different CNN architectures, investigates different optimization algorithms and trainable parameters. The experiments are conducted on two different types of grayscale datasets to make this work more generic and robust. One of the CNN architecture in combination with adadelta optimization achieved a recognition rate of 97.95%. The experimental results demonstrate that CNN based end-to-end learning achieves recognition rates much better than the traditional techniques
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
Multiple classifier fusion using the fuzzy integral.
Fusion of multiple classifier decisions is a powerful method for increasing classification rates in difficult pattern recognition problems. Researchers have found that in many applications it is better to fuse multiple relatively simple classifiers than to build a single sophisticated classifier to achieve better recognition rates. Ideally, the combination function should take advantage of the strengths of individual classifiers and of all possible subsets of classifiers, avoid their weaknesses, and use all the dynamically available knowledge about the inputs, the outputs, the classes, and the classifiers. Automatic reading of handwritten numerals is a difficult problem because of the great variations involved in the shape of the characters. In this thesis an evidence fusion technique, based on the notion of fuzzy integral is utilized to combine the results of different classifiers and realize a robust algorithm for high accuracy handwritten numeral recognition. Both source relevance as well as source evidence are utilized to achieve significant enhancements. The most important advantage of this system is that not only is the evidence combined but that the relative importance of the different sources is also considered. Various conventional and fuzzy integral based fusion methods are explained in detail and experimental results obtained are compared. A method is introduced to improve the fuzzy densities of the classifiers which would improve the fusion results. In this method we use the correction factors obtained from the performance matrices to alter the initial fuzzy densities. Experiments on handwritten numeral recognition are described and compared. These experiments show that very low error rates can be achieved by fusing several low performance classifiers.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1999 .B45. Source: Masters Abstracts International, Volume: 39-02, page: 0558. Adviser: M. Ahmadi. Thesis (M.A.Sc.)--University of Windsor (Canada), 1999
Embedded Large-Scale Handwritten Chinese Character Recognition
As handwriting input becomes more prevalent, the large symbol inventory
required to support Chinese handwriting recognition poses unique challenges.
This paper describes how the Apple deep learning recognition system can
accurately handle up to 30,000 Chinese characters while running in real-time
across a range of mobile devices. To achieve acceptable accuracy, we paid
particular attention to data collection conditions, representativeness of
writing styles, and training regimen. We found that, with proper care, even
larger inventories are within reach. Our experiments show that accuracy only
degrades slowly as the inventory increases, as long as we use training data of
sufficient quality and in sufficient quantity.Comment: 5 pages, 7 figure
Zone-Features based Nearest Neighbor Classification of Images of Kannada Printed and Handwritten Vowel and Consonant Primitives
The characters of any languages having scripts are formed by basic units called primitives. It is necessary to practice writing the primitives and their appropriate combinations while writing different characters. In order to automate character generation, primitives201F; recognition becomes important. In this paper, we propose a zone-features based nearest neighbor classification of Kannada printed and handwritten vowel and consonant primitives. The normalized character image is divided into 49 zones, each of size 4x4 pixels. The classifier based on nearest neighbor using Euclidean distances is deployed. Experiments are performed on images of printed and handwritten primitives of Kannada vowels and consonants. We have considered 9120 images of printed and 3800 images of handwritten 38 primitives. A K-fold cross validation method is used for computation of results. We have observed average recognition accuracies are in the range [90%, 93%] and [93% to 94%] for printed and handwritten primitives respectively. The work is useful in multimedia teaching, animation; Robot based assistance in handwriting, etc
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
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