Learning Representations from Persian Handwriting for Offline Signature Verification, a Deep Transfer Learning Approach

Offline Signature Verification (OSV) is a challenging pattern recognition task, especially when it is expected to generalize well on the skilled forgeries that are not available during the training. Its challenges also include small training sample and large intra-class variations. Considering the limitations, we suggest a novel transfer learning approach from Persian handwriting domain to multi-language OSV domain. We train two Residual CNNs on the source domain separately based on two different tasks of word classification and writer identification. Since identifying a person signature resembles identifying ones handwriting, it seems perfectly convenient to use handwriting for the feature learning phase. The learned representation on the more varied and plentiful handwriting dataset can compensate for the lack of training data in the original task, i.e. OSV, without sacrificing the generalizability. Our proposed OSV system includes two steps: learning representation and verification of the input signature. For the first step, the signature images are fed into the trained Residual CNNs. The output representations are then used to train SVMs for the verification. We test our OSV system on three different signature datasets, including MCYT (a Spanish signature dataset), UTSig (a Persian one) and GPDS-Synthetic (an artificial dataset). On UT-SIG, we achieved 9.80% Equal Error Rate (EER) which showed substantial improvement over the best EER in the literature, 17.45%. Our proposed method surpassed state-of-the-arts by 6% on GPDS-Synthetic, achieving 6.81%. On MCYT, EER of 3.98% was obtained which is comparable to the best previously reported results

Improving Students' Achievement in Writing Desriptive Paragraph by Using Movie Posters

This study aims at improving students' achievement in writing descriptive paragraph by using Movie Posters. This study was conducted by using classroom action research. The subject of the research was class X SMA Negeri I Galang, which consist of 36 students. The research was conducted in two cycles which cycle I and cycle II consist of 3 meetings. The instruments for collecting data were qualitative (interview, diary notes, and observation sheet) and quantitative data(writing test). Based on the data analysis, the mean of students' score in Test I was 56,38 ; for the Test II was 68,05 , and for the Test III was 85. The qualitative data showed that the students were interested by using Movie Posters. The conclusion is that by using Movie Posters improves the students' achievement in writing descriptive

A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pre-train models on images from a different domain and then fine-tune them on historical documents. In the current research, a typical example of such cross-domain transfer learning is the use of neural networks that have been pre-trained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pre-training helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pre-training for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and content-based retrieval. While we obtain mixed results for semantic segmentation at pixel-level, we observe a clear trend across different network architectures that ImageNet pre-training has a positive effect on classification as well as content-based retrieval

The Discourse of Digital Deceptions and ‘419’ Emails

This study applies a computer-mediated discourse analysis (CMDA) to the study of discourse structures and functions of ‘419’ emails – the Nigerian term for online/financial fraud. The hoax mails are in the form of online lottery winning announcements, and email ‘business proposals’ involving money transfers/claims of dormant bank accounts overseas. Data comprise 68 email samples collected from the researcher’s inboxes and colleagues’ and students’ mail boxes between January 2008 and March 2009 in Ota, Nigeria. The study reveals that the writers of the mails apply discourse/pragmatic strategies such as socio-cultural greeting formulas,self-identification, reassurance/confidence building, narrativity and action prompting strategies to sustain the interest of the receivers. The study also shows that this genre of computer-mediated communication (CMC) has become a regular part of our Internet experience, and is not likely to be extinct in the near future as previous studies of email hoaxes have predicted. It is believed that as the global economy witnesses a recession, chances are that more creative and complex ways of combating the situation will arise. Economic hardship has been blamed for fraud/online scams, inadvertently prompting youths to engage in various anti-social activities. K E Y W O R D S : computer-media communication, deceptions, discourse, email, ‘419’, fraud, hoax

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

Spartan Daily, August 27, 2003

Volume 121, Issue 2https://scholarworks.sjsu.edu/spartandaily/9868/thumbnail.jp