163 research outputs found
Deep-Learning Inferencing with High-Performance Hardware Accelerators
In order to improve their performance-per-watt capabilities over general-purpose architectures, FPGAs are commonly employed to accelerate applications. With the exponential growth of available data, machine-learning apps have generated greater interest in order to better understand that data and increase autonomous processing. As FPGAs become more readily available through cloud services like Amazon Web Services F1 platform, it is worth studying the performance of accelerating machine-learning apps on FPGAs over traditional fixed-logic devices, like CPUs and GPUs. FPGA frameworks for accelerating convolutional neural networks, which are used in many machine-learning apps, have started emerging for accelerated-application development. This thesis aims to compare the performance of these emerging frameworks on two commonly-used convolutional neural networks, GoogLeNet and AlexNet. Specifically, handwritten Chinese character recognition is benchmarked across multiple currently available FPGA frameworks on Xilinx and Intel FPGAs and compared against multiple CPU and GPU architectures featured on AWS, Google’s Cloud platform, the University of Pittsburgh’s Center for Research Computing (CRC), and Intel’s vLab Academic Cluster. All NVIDIA GPUs have proven to have the best performance over every other device in this study. The Zebra framework available for Xilinx FPGAs showed to have an average 8.3× and 9.3× better performance and efficiency, respectively, over the OpenVINO framework available for Intel FPGAs. Although the Zebra framework on the Xilinx VU9P showed better efficiency than the Pascal-based GPUs, the NVIDIA Tesla V100 proved to be the most efficient device at 125.9 and 47.2 images-per-second-per-Watt for AlexNet and GoogLeNet, respectively. Although currently lacking, FPGA frameworks and devices have the potential to compete with GPUs in terms of performance and efficiency
Generating Handwritten Chinese Characters using CycleGAN
Handwriting of Chinese has long been an important skill in East Asia.
However, automatic generation of handwritten Chinese characters poses a great
challenge due to the large number of characters. Various machine learning
techniques have been used to recognize Chinese characters, but few works have
studied the handwritten Chinese character generation problem, especially with
unpaired training data. In this work, we formulate the Chinese handwritten
character generation as a problem that learns a mapping from an existing
printed font to a personalized handwritten style. We further propose DenseNet
CycleGAN to generate Chinese handwritten characters. Our method is applied not
only to commonly used Chinese characters but also to calligraphy work with
aesthetic values. Furthermore, we propose content accuracy and style
discrepancy as the evaluation metrics to assess the quality of the handwritten
characters generated. We then use our proposed metrics to evaluate the
generated characters from CASIA dataset as well as our newly introduced Lanting
calligraphy dataset.Comment: Accepted at WACV 201
The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
Offline handwritten Chinese recognition is an important research area of pattern recognition, including offline handwritten Chinese character recognition (offline HCCR) and offline handwritten Chinese text recognition (offline HCTR), which are closely related to daily life. With new deep learning techniques and the combination with other domain knowledge, offline handwritten Chinese recognition has gained breakthroughs in methods and performance in recent years. However, there have yet to be articles that provide a technical review of this field since 2016. In light of this, this paper reviews the research progress and challenges of offline handwritten Chinese recognition based on traditional techniques, deep learning methods, methods combining deep learning with traditional techniques, and knowledge from other areas from 2016 to 2022. Firstly, it introduces the research background and status of handwritten Chinese recognition, standard datasets, and evaluation metrics. Secondly, a comprehensive summary and analysis of offline HCCR and offline HCTR approaches during the last seven years is provided, along with an explanation of their concepts, specifics, and performances. Finally, the main research problems in this field over the past few years are presented. The challenges still exist in offline handwritten Chinese recognition are discussed, aiming to inspire future research work
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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