202 research outputs found
Handwriting styles: benchmarks and evaluation metrics
Evaluating the style of handwriting generation is a challenging problem,
since it is not well defined. It is a key component in order to develop in
developing systems with more personalized experiences with humans. In this
paper, we propose baseline benchmarks, in order to set anchors to estimate the
relative quality of different handwriting style methods. This will be done
using deep learning techniques, which have shown remarkable results in
different machine learning tasks, learning classification, regression, and most
relevant to our work, generating temporal sequences. We discuss the challenges
associated with evaluating our methods, which is related to evaluation of
generative models in general. We then propose evaluation metrics, which we find
relevant to this problem, and we discuss how we evaluate the evaluation
metrics. In this study, we use IRON-OFF dataset. To the best of our knowledge,
there is no work done before in generating handwriting (either in terms of
methodology or the performance metrics), our in exploring styles using this
dataset.Comment: Submitted to IEEE International Workshop on Deep and Transfer
Learning (DTL 2018
Style Transfer and Extraction for the Handwritten Letters Using Deep Learning
How can we learn, transfer and extract handwriting styles using deep neural
networks? This paper explores these questions using a deep conditioned
autoencoder on the IRON-OFF handwriting data-set. We perform three experiments
that systematically explore the quality of our style extraction procedure.
First, We compare our model to handwriting benchmarks using multidimensional
performance metrics. Second, we explore the quality of style transfer, i.e. how
the model performs on new, unseen writers. In both experiments, we improve the
metrics of state of the art methods by a large margin. Lastly, we analyze the
latent space of our model, and we see that it separates consistently writing
styles.Comment: Accepted in ICAART 201
Entropy reduction via simplified image contourization
The process of contourization is presented which converts a raster image into a set of plateaux or contours. These contours can be grouped into a hierarchical structure, defining total spatial inclusion, called a contour tree. A contour coder has been developed which fully describes these contours in a compact and efficient manner and is the basis for an image compression method. Simplification of the contour tree has been undertaken by merging contour tree nodes thus lowering the contour tree's entropy. This can be exploited by the contour coder to increase the image compression ratio. By applying general and simple rules derived from physiological experiments on the human vision system, lossy image compression can be achieved which minimizes noticeable artifacts in the simplified image
Recognition of online handwritten music symbols
Paper submitted to MML 2013, 6th International Workshop on Machine Learning and Music, Prague, September 23, 2013.An effective way of digitizing a new musical composition is to use an e-pen and tablet application in which the user's pen strokes are recognized online and the digital score is created with the sole effort of the composition itself. This work aims to be a starting point for research on the recognition of online handwritten music notation. To this end, different alternatives within the two modalities of recognition resulting from this data are presented: online recognition, which uses the strokes marked by a pen, and offline recognition, which uses the image generated after drawing the symbol. A comparative experiment with common machine learning algorithms over a dataset of 3800 samples and 32 different music symbols is presented. Results show that samples of the actual user are needed if good classification rates are pursued. Moreover, algorithms using the online data, on average, achieve better classification results than the others
Picture words with invisible lines
AbstractA picture word is a word over the alphabet {r, r̄, u, ū, rb, r̄b, ub, ūb}. With any picture word, we associate a picture as follows: the reading of each letter of the word induces a unit move; the letters r and rb (r̄ and r̄b, u and ub, ū and ūb) stand for a right (left, up, down) move; for each letter from {r, r̄, u, ū}, we move by drawing a unit line; for the other letters, we move with “pen-up”. We present a rewriting system S which generates exactly all the picture words describing a given picture
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