1,463 research outputs found
Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
Online handwritten Chinese text recognition (OHCTR) is a challenging problem
as it involves a large-scale character set, ambiguous segmentation, and
variable-length input sequences. In this paper, we exploit the outstanding
capability of path signature to translate online pen-tip trajectories into
informative signature feature maps using a sliding window-based method,
successfully capturing the analytic and geometric properties of pen strokes
with strong local invariance and robustness. A multi-spatial-context fully
convolutional recurrent network (MCFCRN) is proposed to exploit the multiple
spatial contexts from the signature feature maps and generate a prediction
sequence while completely avoiding the difficult segmentation problem.
Furthermore, an implicit language model is developed to make predictions based
on semantic context within a predicting feature sequence, providing a new
perspective for incorporating lexicon constraints and prior knowledge about a
certain language in the recognition procedure. Experiments on two standard
benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with
correct rates of 97.10% and 97.15%, respectively, which are significantly
better than the best result reported thus far in the literature.Comment: 14 pages, 9 figure
On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net
On-line handwritten scripts are usually dealt with pen
tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this
paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple hresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples
Arabic Handwritten Word Recognition based on Bernoulli Mixture HMMs
This thesis presents new approaches in off-line Arabic Handwriting Recognition based on
conventional Bernoulli Hidden Markov models. Until now, the off-line handwriting
recognition, in particular, the Arabic handwriting recognition is still far away form being
perfect. Hidden Markov Models (HMMs) are now widely used for off-line handwriting
recognition in many languages and, in particular, in Arabic. As in speech recognition, they
are usually built from shared, embedded HMMs at symbol level, in which state-conditional
probability density functions are modeled with Gaussian mixtures. In contrast to speech
recognition, however, it is unclear which kind of features should be used and, indeed, very
different features sets are in use today. Among them, we have recently proposed to simply
use columns of raw, binary image pixels, which are directly fed into embedded Bernoulli
(mixture) HMMs, that is, embedded HMMs in which the emission probabilities are modeled
with Bernoulli mixtures. The idea is to by-pass feature extraction and ensure that no
discriminative information is filtered out during feature extraction, which in some sense is
integrated into the recognition model. In this thesis, we review this idea along with some
extensions that are currently providing state-of-the-art results on Arabic handwritten word
recognition.Alkhoury, I. (2010). Arabic Handwritten Word Recognition based on Bernoulli Mixture HMMs. http://hdl.handle.net/10251/11478Archivo delegad
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