587 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
SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-world Verification
An open research problem in automatic signature verification is the skilled
forgery attacks. However, the skilled forgeries are very difficult to acquire
for representation learning. To tackle this issue, this paper proposes to learn
dynamic signature representations through ranking synthesized signatures.
First, a neuromotor inspired signature synthesis method is proposed to
synthesize signatures with different distortion levels for any template
signature. Then, given the templates, we construct a lightweight
one-dimensional convolutional network to learn to rank the synthesized samples,
and directly optimize the average precision of the ranking to exploit relative
and fine-grained signature similarities. Finally, after training, fixed-length
representations can be extracted from dynamic signatures of variable lengths
for verification. One highlight of our method is that it requires neither
skilled nor random forgeries for training, yet it surpasses the
state-of-the-art by a large margin on two public benchmarks.Comment: To appear in AAAI 202
GROUNDTRUTH GENERATION AND DOCUMENT IMAGE DEGRADATION
The problem of generating synthetic data for the training and evaluation of document analysis systems has been widely addressed in recent years. With the increased interest in processing multilingual sources, however, there is a tremendous need to be able to rapidly generate data in new languages and scripts, without the need to develop specialized systems. We have developed a system, which uses language support of the MS Windows operating system combined with custom print drivers to render tiff images simultaneously with windows Enhanced Metafile directives. The metafile information is parsed to generate zone, line, word, and character ground truth including location, font information and content in any language supported by Windows. The resulting images can be physically or synthetically degraded by our degradation modules, and used for training and evaluating Optical Character Recognition (OCR) systems. Our document image degradation methodology incorporates several often-encountered types of noise at the page and pixel levels. Examples of OCR evaluation and synthetically degraded document images are given to demonstrate the effectiveness
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