Confidence modeling for verification post-processing for handwriting recognition - Trang 30-35
J.F. Pitrelli, M.P. Perrone
We apply confidence-scoring techniques to verify the output of a handwriting
recognizer. We evaluate a variety of scoring functions, including likelihood
ratios and estimated posterior probabilities of correctness, in a postprocessing
mode to generate confidence scores at the character or word level. Using the
post-processor in conjunction with an HMM-based on-line handwriting recognizer
for large... hiện toàn bộ
#Handwriting recognition #Character recognition #Character generation #Hidden Markov models #Error correction #Error analysis #Costs #Automation #Humans #Vocabulary
Xác thực người viết và nén phân đoạn Dịch bởi AI - Trang 434-439
A. Seropian, N. Vincent
Trong bài báo này, chúng tôi đề xuất một phương pháp mới cho phép xác thực một
người viết. Để đạt được mục tiêu này, chúng tôi sử dụng các thuộc tính tự tương
tự trong các tác phẩm viết, tức là chúng tôi trích xuất một số phần không thay
đổi của văn bản, các hình dạng không thay đổi đặc trưng cho các văn bản của một
người viết. Từ góc độ thực tiễn, những hình dạng không thay đổi này được xác
định ... hiện toàn bộ
#Authentication #Fractals #Writing #Handwriting recognition #Image coding #Reactive power #Shape #Pattern analysis #Robustness #Pattern recognition
A comparative study on mirror image learning and ALSM - Trang 151-156
T. Wakabayashi, Meng Shi, W. Ohyama, F. Kimura
In this paper, the effectiveness of a corrective learning algorithm MIL (mirror
image learning) is comparatively studied with that of ALSM (average learning
subspace method). Both MIL and ALSM were proposed to improve the learning
effectiveness of class conditional distributions. While the ALSM modifies the
basis vectors of a subspace by subtracting the autocorrelation matrix for
counter classes f... hiện toàn bộ
#Mirrors #Laser radar #Autocorrelation #Counting circuits #Handwriting recognition #Image generation #Testing #Euclidean distance #Vectors #Covariance matrix
Nhận diện chữ Hiragana viết tay sử dụng máy vector hỗ trợ Dịch bởi AI - Trang 55-60
K.-I. Maruyama, M. Maruyama, H. Miyao, Y. Nakano
Bài báo mô tả một phương pháp nhằm cải thiện tỷ lệ nhận diện tích lũy của việc
nhận diện mẫu sử dụng đồ thị không tuần hoàn định hướng quyết định (DDAG) dựa
trên máy vector hỗ trợ (SVM). Mặc dù DDAG ban đầu có hiệu suất cao và tốc độ
thực thi nhanh, nhưng nó không xem xét tỷ lệ nhận diện tích lũy. Chúng tôi xây
dựng một DDAG có thể kết hợp tỷ lệ nhận diện tích lũy. Kết quả thí nghiệm của
chúng tôi... hiện toàn bộ
#Support vector machines #Support vector machine classification #Character recognition #Pattern recognition #Character generation #Kernel #Voting #Quadratic programming #Lagrangian functions #Conferences
Hidden loop recovery for handwriting recognition - Trang 375-380
D. Doermann, N. Intrator, E. Rivin, T. Steinherz
One significant challenge in the recognition of off-line handwriting is in the
interpretation of loop structures. Although this information is readily
available in online representation, close proximity of strokes often merges
their centers making them difficult to identify. In this paper a novel approach
to the recovery of hidden loops in off-line scanned document images is
presented. The propose... hiện toàn bộ
#Handwriting recognition #Computer science #Writing #Educational institutions #Cities and towns #Distance measurement #Shape measurement #Character recognition #Topology #Signal to noise ratio
Confident assessment of children's handwritten responses - Trang 508-512
J. Allan, T. Allen, N. Sherkat
This paper introduces a novel approach for the automatic assessment of
children's responses to standardised English exam questions. The constrained
nature of the question and answer medium is exploited to produce an automatic
assessment mechanism that is both highly accurate and produces a reasonable
level of response yield. It is shown that the novel approach can achieve 100%
scoring accuracy on ... hiện toàn bộ
#Iris #Error analysis #Automatic testing #Aging #Publishing #Conferences #Handwriting recognition #Maintenance #Shape #Writing
Learning-based cursive handwriting synthesis - Trang 157-162
Jue Wang, Chenyu Wu, Ying-Qing Xu, Heung-Yeung Shum, Liang Ji
In this paper an integrated approach for modeling, learning and synthesizing
personal cursive handwriting is proposed. Cursive handwriting is modeled by a
tri-unit handwriting model, which focuses on both the handwritten letters and
the interconnection strokes of adjacent letters. Handwriting strokes are formed
from generative models that are based on control points and B-spline curves. In
the two... hiện toàn bộ
#Deformable models #Writing #Shape #Asia #Spline #Handwriting recognition #Mathematical model #Automation #Data mining #Control system synthesis
Detecting dominant points on online scripts with a simple approach - Trang 351-356
Su Yang, Guozhong Dai
We proposed a new dominant point detection method which has the following
advantages: robust, computational efficient, and real-time response to pen
movement. We construct a variable which is the ratio of the height to the width
of an imagined rectangle whose bottom coincides with the polygon enclosed by the
pen movement trace, and the area is equal to the polygonal area. While online
watching whe... hiện toàn bộ
#Turning #Handwriting recognition #Fluctuations #Robustness #Detectors #Computational efficiency #Computational complexity #Conferences #Application software #Computer graphics
Script and nature differentiation for Arabic and Latin text images - Trang 309-313
S. Kanoun, A. Ennaji, Y. Lecourtier, A.M. Alimi
A method for Arabic and Latin text block differentiation for printed and
handwritten scripts is proposed. This method is based on a morphological
analysis for each script at the text block level and a geometrical analysis at
the line and the connected component level. In this paper, we present a brief
survey, of existing methods used for scripts differentiation as well as a
general characteristics... hiện toàn bộ
#Text analysis #Handwriting recognition #Laboratories #Machine intelligence #Optical character recognition software #Natural languages #Optical devices #Optical sensors #Conferences #Feature extraction