2,611 research outputs found
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition
Handwritten Text Recognition (HTR) is still a challenging problem because it
must deal with two important difficulties: the variability among writing
styles, and the scarcity of labelled data. To alleviate such problems,
synthetic data generation and data augmentation are typically used to train HTR
systems. However, training with such data produces encouraging but still
inaccurate transcriptions in real words. In this paper, we propose an
unsupervised writer adaptation approach that is able to automatically adjust a
generic handwritten word recognizer, fully trained with synthetic fonts,
towards a new incoming writer. We have experimentally validated our proposal
using five different datasets, covering several challenges (i) the document
source: modern and historic samples, which may involve paper degradation
problems; (ii) different handwriting styles: single and multiple writer
collections; and (iii) language, which involves different character
combinations. Across these challenging collections, we show that our system is
able to maintain its performance, thus, it provides a practical and generic
approach to deal with new document collections without requiring any expensive
and tedious manual annotation step.Comment: Accepted to WACV 202
A Combined Crisp and Fuzzy Approach for Handwriting Analysis
This paper presents an off-line writer-independent handwriting analysis system which utilizes both classical crisp and fuzzy methodologies to output possible personality traits of the writer. The design deploys an analytical handwriting analysis approach based on two primitives, the baseline and the slant angle of the characters. The objective of the design strategy is to present a group of parameters for handwriting analysis based on the text. These parameters allow for the classification of writing into different categories which could be used as a preliminary step for outputting the personality traits of the writer. Two parameters, the baseline and the slant-angle, are the inputs to a rule-base which outputs the personality trait category. The evaluation of the baseline is non-fuzzy (crisp) whereas the evaluation of the slant-angle utilizes the fuzzy paradigm.
The approach is based on a combination of classical geometric arithmetic evaluation and fuzzy control designs. For determination of the base line angle two methodologies are explored: the geometric-features based segmentation method and a method based on biologically inspired generation theories or the low pass filtering method. We utilize the geometric features evaluation for the baseline extraction since it proves more robust with respect to the variations of the handwriting in an off-line environment.
For determination of the slant type a fuzzy technique is adopted to determine the contributions of the slant-type angle to each of the five variations of the slant-type categories. The uncertainties in the system model are expressed by fuzzy-valued model parameters with their membership functions derived from experimental data. In total five variations of slant type are considered. These include extreme left, controlled left, vertical, controlled right and extreme right.
Fifteen personality traits PT1 - PT15 were identified and sets of rules formulation were created, (e.g., If Input1 is level and Input2 is Controlled Left then Output is PTx.)
The proposed approach takes advantage of two differing methodologies that have clear outputs to evaluate two attributes of handwriting. The outputs are utilized to determine a personality trait. The system can be further enhanced by including more parameters such as size of letters, spacing between letters and other attributes of handwriting as part of the inputs for trait determination
GR-RNN:Global-Context Residual Recurrent Neural Networks for Writer Identification
This paper presents an end-to-end neural network system to identify writers
through handwritten word images, which jointly integrates global-context
information and a sequence of local fragment-based features. The global-context
information is extracted from the tail of the neural network by a global
average pooling step. The sequence of local and fragment-based features is
extracted from a low-level deep feature map which contains subtle information
about the handwriting style. The spatial relationship between the sequence of
fragments is modeled by the recurrent neural network (RNN) to strengthen the
discriminative ability of the local fragment features. We leverage the
complementary information between the global-context and local fragments,
resulting in the proposed global-context residual recurrent neural network
(GR-RNN) method. The proposed method is evaluated on four public data sets and
experimental results demonstrate that it can provide state-of-the-art
performance. In addition, the neural networks trained on gray-scale images
provide better results than neural networks trained on binarized and contour
images, indicating that texture information plays an important role for writer
identification.
The source code will be available:
\url{https://github.com/shengfly/writer-identification}.Comment: To appear: Pattern Recognitio
Analysis by synthesis in handwriting recognition
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 34-38).by Boris L. Elbert.M.S
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