A template-based recognition system for on-line handwritten characters

New developments on our real-time recognition engine for isolated handwritten characters are presented. This engine is based on approximate Dynamic Time Warping comparisons with prototypes selected by fast, less accurate classification procedures. The error rate it currently obtains on the standard Pendigits task, 0.60%, is significantly lower than both the error rate of other recently published techniques and the one we obtain from the recognition engine included in the Microsoft Vista operating syste

Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions

Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability -- they are particularly difficult to understand because of their non-linear nature. As a result, neural networks are often treated as "black box" models, and in the past, have been trained purely to optimize the accuracy of predictions. In this work, we create a novel network architecture for deep learning that naturally explains its own reasoning for each prediction. This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input. The encoder of the autoencoder allows us to do comparisons within the latent space, while the decoder allows us to visualize the learned prototypes. The training objective has four terms: an accuracy term, a term that encourages every prototype to be similar to at least one encoded input, a term that encourages every encoded input to be close to at least one prototype, and a term that encourages faithful reconstruction by the autoencoder. The distances computed in the prototype layer are used as part of the classification process. Since the prototypes are learned during training, the learned network naturally comes with explanations for each prediction, and the explanations are loyal to what the network actually computes.Comment: The first two authors contributed equally, 8 pages, accepted in AAAI 201

Recognition of handwritten digits using proximal support vector machine

Handwritten Digit Recognition System involves reception and interpretation of handwritten digits by a machine. Due to variation in shape and orientation of handwritten digits, it is difficult for a machine to interpret handwritten digits. Handwritten digit Recognition has a wide area of research due to its vast applications like automatic bank cheques processing, billing and automatic postal service. In this thesis, an Offline Handwritten Digit Recognition System is presented. The recognition system is broadly divided into 2 parts, first part is feature extraction from handwritten images and the second one is classification of feature vector into digits. We propose descriptors for handwritten digit recognition based on Histogram of Oriented Gradient (HOG) feature .It is one of the widely used feature vector for object detection in computer vision. For classification of features, linear Proximal Support Vector Machine (PSVM) Classifier is proposed. This is a binary class classifier which is further converted to a 10 class classifier by means of One against all algorithm. Due to small training time, PSVM classifier is preferable over standard Support Vector Machine (SVM) Classifier. The handwritten images both for training and testing are taken from MNIST database. The performance of the system is measured in terms of Sensitivity, Accuracy, Positive Predictivity and Specificity. The performance of PSVM classifier is better compared to Artificial Neural Network(ANN)

A Hybrid Artificial Neural Network Model For Data Visualisation, Classification, And Clustering [QP363.3. T253 2006 f rb].

Tesis ini mempersembahkan penyelidikan tentang satu model hibrid rangkaian neural buatan yang boleh menghasilkan satu peta pengekalan-topologi, serupa dengan penerangan teori bagi peta otak, untuk visualisasi, klasifikasi dan pengklusteran data. In this thesis, the research of a hybrid Artificial Neural Network (ANN) model that is able to produce a topology-preserving map, which is akin to the theoretical explanation of the brain map, for data visualisation, classification, and clustering is presented