Special Radical Detection by Statistical Classification for On-line Handwritten Chinese Character Recognition

International audienceThe hierarchical nature of Chinese characters has inspired radical-based recognition, but radical segmentation from characters remains a challenge. We previously proposed a radical-based approach for on-line handwritten Chinese character recognition, which incorporates character structure knowledge into integrated radical segmentation and recognition, and performs well on characters of left-right and up-down structures (non-special structures). In this paper, we propose a statistical-classification-based method for detecting special radicals from special-structure characters. We design 19 binary classifiers for classifying candidate radicals (groups of strokes) hypothesized from the input character. Characters with special radicals detected are recognized using special-structure models, while those without special radicals are recognized using the models for non-special structures. We applied the recognition framework to 6,763 character classes, and achieved promising recognition performance in experiments

Robust recognition technique for handwritten Kannada character recognition using capsule networks

Automated reading of handwritten Kannada documents is highly challenging due to the presence of vowels, consonants and its modifiers. The variable nature of handwriting styles aggravates the complexity of machine based reading of handwritten vowels and consonants. In this paper, our investigation is inclined towards design of a deep convolution network with capsule and routing layers to efficiently recognize  Kannada handwritten characters.  Capsule network architecture is built of an input layer,  two convolution layers, primary capsule, routing capsule layers followed by tri-level dense convolution layer and an output layer.  For experimentation, datasets are collected from more than 100 users for creation of training data samples of about 7769 comprising of 49 classes. Test samples of all the 49 classes are again collected separately from 3 to 5 users creating a total of 245 samples for novel patterns. It is inferred from performance evaluation; a loss of 0.66% is obtained in the classification process and for 43 classes precision of 100% is achieved with an accuracy of 99%. An average accuracy of 95% is achieved for all remaining 6 classes with an average precision of 89%

Feature Extraction Methods for Character Recognition

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Flexible Image Recognition Software Toolbox (First)

The deep convolutional neural network is a new concept in the neural network field, and research is still going on to improve network performance. These networks are used for recognizing patterns in data, as they provide shift invariance, automatic extraction of local features, by using local receptive fields, and improved generalization, by using weight sharing techniques. The main purpose of this thesis is to create a Flexible Image Recognition Software Toolbox (FIRST), which is a software package that allows users to build custom deep networks, while also having ready-made versions of popular deep networks, such as Lecun's LeNet-1, LeNet-5 and LeNet-7. This software package is created for designing, training and simulating deep networks. The goal is to reduce the amount of time required by users to implement any particular network. To design this software package, a general modular framework is introduced, in which simulation and gradient calculations are derived. Due to this modularity and generality, FIRST provides flexibility to users in easily designing specific complex or deep networks. FIRST includes several training algorithms, such as Resilient Backpropagation, Scaled Conjugate Gradient and Steepest Descent. This thesis also describes the usage of the FIRST software and the design of functions used in the software. It also provides information about how to create custom networks. The thesis includes two sample training sessions that demonstrate how to use the FIRST software. One example is phoneme recognition in 1D speech data. The second example is handwritten digit recognition in 2D images.Electrical Engineerin

Large-scale document labeling using supervised sequence embedding

A critical component in computational treatment of an automated document labeling is the choice of an appropriate representation. Proper representation captures specific phenomena of interest in data while transforming it to a format appropriate for a classifier. For a text document, a popular choice is the bag-of-words (BoW) representation that encodes presence of unique words with non-zero weights such as TF-IDF. Extending this model to long, overlapping phrases (n-grams) results in exponential explosion in the dimensionality of the representation. In this work, we develop a model that encodes long phrases in a low-dimensional latent space with a cumulative function of individual words in each phrase. In contrast to BoW, the parameter space of the proposed model grows linearly with the length of the phrase. The proposed model requires only vector additions and multiplications with scalars to compute the latent representation of phrases, which makes it applicable to large-scale text labeling problems. Several sentiment classification and binary topic categorization problems will be used to empirically evaluate the proposed representation. The same model can also encode relative spatial distribution of elements in higher-dimensional sequences. In order to verify this claim, the proposed model will be evaluated on a large-scale image classification dataset, where images are transformed into two-dimensional sequences of quantized image descriptors.Ph.D., Computer Science -- Drexel University, 201