59 research outputs found
Template-Instance Loss for Offline Handwritten Chinese Character Recognition
The long-standing challenges for offline handwritten Chinese character
recognition (HCCR) are twofold: Chinese characters can be very diverse and
complicated while similarly looking, and cursive handwriting (due to increased
writing speed and infrequent pen lifting) makes strokes and even characters
connected together in a flowing manner. In this paper, we propose the template
and instance loss functions for the relevant machine learning tasks in offline
handwritten Chinese character recognition. First, the character template is
designed to deal with the intrinsic similarities among Chinese characters.
Second, the instance loss can reduce category variance according to
classification difficulty, giving a large penalty to the outlier instance of
handwritten Chinese character. Trained with the new loss functions using our
deep network architecture HCCR14Layer model consisting of simple layers, our
extensive experiments show that it yields state-of-the-art performance and
beyond for offline HCCR.Comment: Accepted by ICDAR 201
A Novel Deep Convolutional Neural Network Architecture Based on Transfer Learning for Handwritten Urdu Character Recognition
Deep convolutional neural networks (CNN) have made a huge impact on computer vision and set the state-of-the-art in providing extremely definite classification results. For character recognition, where the training images are usually inadequate, mostly transfer learning of pre-trained CNN is often utilized. In this paper, we propose a novel deep convolutional neural network for handwritten Urdu character recognition by transfer learning three pre-trained CNN models. We fine-tuned the layers of these pre-trained CNNs so as to extract features considering both global and local details of the Urdu character structure. The extracted features from the three CNN models are concatenated to train with two fully connected layers for classification. The experiment is conducted on UNHD, EMILLE, DBAHCL, and CDB/Farsi dataset, and we achieve 97.18% average recognition accuracy which outperforms the individual CNNs and numerous conventional classification methods
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
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