Automatic Chinese Postal Address Block Location Using Proximity Descriptors and Cooperative Profit Random Forests.

Locating the destination address block is key to automated sorting of mails. Due to the characteristics of Chinese envelopes used in mainland China, we here exploit proximity cues in order to describe the investigated regions on envelopes. We propose two proximity descriptors encoding spatial distributions of the connected components obtained from the binary envelope images. To locate the destination address block, these descriptors are used together with cooperative profit random forests (CPRFs). Experimental results show that the proposed proximity descriptors are superior to two component descriptors, which only exploit the shape characteristics of the individual components, and the CPRF classifier produces higher recall values than seven state-of-the-art classifiers. These promising results are due to the fact that the proposed descriptors encode the proximity characteristics of the binary envelope images, and the CPRF classifier uses an effective tree node split approach

Handwritten Digit Recognition and Classification Using Machine Learning

In this paper, multiple learning techniques based on Optical character recognition (OCR) for the handwritten digit recognition are examined, and a new accuracy level for recognition of the MNIST dataset is reported. The proposed framework involves three primary parts, image pre-processing, feature extraction and classification. This study strives to improve the recognition accuracy by more than 99% in handwritten digit recognition. As will be seen, pre-processing and feature extraction play crucial roles in this experiment to reach the highest accuracy

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy

Feature design and lexicon reduction for efficient offline handwriting recognition

This thesis establishes a pattern recognition framework for offline word recognition systems. It focuses on the image level features because they greatly influence the recognition performance. In particular, we consider two complementary aspects of prominent features impact: lexicon reduction and the actual recognition. The first aspect, lexicon reduction, consists in the design of a weak classifier which outputs a set of candidate word hypotheses given a word image. Its main purpose is to reduce the recognition computational time while maintaining (or even improving) the recognition rate. The second aspect is the actual recognition system itself. In fact, several features exist in the literature based on different fields of research, but no consensus exists concerning the most promising ones. The goal of the proposed framework is to improve our understanding of relevant features in order to build better recognition systems. For this purpose, we addressed two specific problems: 1) feature design for lexicon reduction (application to Arabic script), and 2) feature evaluation for cursive handwriting recognition (application to Latin and Arabic scripts). Few methods exist for lexicon reduction in Arabic script, unlike Latin script. Existing methods use salient features of Arabic words such as the number of subwords and diacritics, but totally ignore the shape of the subwords. Therefore, our first goal is to perform lexicon reductionn based on subwords shape. Our approach is based on shape indexing, where the shape of a query subword is compared to a labeled database of sample subwords. For efficient comparison with a low computational overhead, we proposed the weighted topological signature vector (W-TSV) framework, where the subword shape is modeled as a weighted directed acyclic graph (DAG) from which the W-TSV vector is extracted for efficient indexing. The main contributions of this work are to extend the existing TSV framework to weighted DAG and to propose a shape indexing approach for lexicon reduction. Good performance for lexicon reduction is achieved for Arabic subwords. Nevertheless, the performance remains modest for Arabic words. Considering the results of our first work on Arabic lexicon reduction, we propose to build a new index for better performance at the word level. The subword shape and the number of subwords and diacritics are all important components of Arabic word shape. We therefore propose the Arabic word descriptor (AWD) which integrates all the aforementioned components. It is built in two steps. First, a structural descriptor (SD) is computed for each connected component (CC) of the word image. It describes the CC shape using the bag-of-words model, where each visual word represents a different local shape structure. Then, the AWD is formed by concatenating the SDs using an efficient heuristic, implicitly discriminating between subwords and diacritics. In the context of lexicon reduction, the AWD is used to index a reference database. The main contribution of this work is the design of the AWD, which integrates lowlevel cues (subword shape structure) and symbolic information (subword counts and diacritics) into a single descriptor. The proposed method has a low computational overhead, it is simple to implement and it provides state-of-the-art performance for lexicon reduction on two Arabic databases, namely the Ibn Sina database of subwords and the IFN/ENIT database of words. The last part of this thesis focuses on features for word recognition. A large body of features exist in the literature, each of them being motivated by different fields, such as pattern recognition, computer vision or machine learning. Identifying the most promising approaches would improve the design of the next generation of features. Nevertheless, because they are based on different concepts, it is difficult to compare them on a theoretical ground and efficient empirical tools are needed. Therefore, the last objective of the thesis is to provide a method for feature evaluation that assesses the strength and complementarity of existing features. A combination scheme has been designed for this purpose, in which each feature is evaluated through a reference recognition system, based on recurrent neural networks. More precisely, each feature is represented by an agent, which is an instance of the recognition system trained with that feature. The decisions of all the agents are combined using a weighted vote. The weights are jointly optimized during a training phase in order to increase the weighted vote of the true word label. Therefore, they reflect the strength and complementarity of the agents and their features for the given task. Finally, they are converted into a numerical score assigned to each feature, which is easy to interpret under this combination model. To the best of our knowledge, this is the first feature evaluation method able to quantify the importance of each feature, instead of providing a ranking based on the recognition rate. Five state-of-the-art features have been tested, and our results provide interesting insight for future feature design

Information Preserving Processing of Noisy Handwritten Document Images

Many pre-processing techniques that normalize artifacts and clean noise induce anomalies due to discretization of the document image. Important information that could be used at later stages may be lost. A proposed composite-model framework takes into account pre-printed information, user-added data, and digitization characteristics. Its benefits are demonstrated by experiments with statistically significant results. Separating pre-printed ruling lines from user-added handwriting shows how ruling lines impact people\u27s handwriting and how they can be exploited for identifying writers. Ruling line detection based on multi-line linear regression reduces the mean error of counting them from 0.10 to 0.03, 6.70 to 0.06, and 0.13 to 0.02, com- pared to an HMM-based approach on three standard test datasets, thereby reducing human correction time by 50%, 83%, and 72% on average. On 61 page images from 16 rule-form templates, the precision and recall of form cell recognition are increased by 2.7% and 3.7%, compared to a cross-matrix approach. Compensating for and exploiting ruling lines during feature extraction rather than pre-processing raises the writer identification accuracy from 61.2% to 67.7% on a 61-writer noisy Arabic dataset. Similarly, counteracting page-wise skew by subtracting it or transforming contours in a continuous coordinate system during feature extraction improves the writer identification accuracy. An implementation study of contour-hinge features reveals that utilizing the full probabilistic probability distribution function matrix improves the writer identification accuracy from 74.9% to 79.5%

A high level approach to Arabic sentence recognition

The aim of this work is to develop sentence recognition system inspired by the human reading process. Cognitive studies observed that the human tended to read a word as a whole at a time. He considers the global word shapes and uses contextual knowledge to infer and discriminate a word among other possible words. The sentence recognition system is a fully integrated system; a word level recogniser (baseline system) integrated with linguistic knowledge post-processing module. The presented baseline system is holistic word-based recognition approach characterised as probabilistic ranked task. The output of the system is multiple recognition hypotheses (N-best word lattice). The basic unit is the word rather than the character; it does not rely on any segmentation or require baseline detection. The considered linguistic knowledge to re-rank the output of the existing baseline system is the standard n-gram Statistical Language Models (SLMs). The candidates are re-ranked through exploiting phrase perplexity score. The system is an OCR system that depends on HMM models utilizing the HTK Toolkit. The baseline system supported by global transformation features extracted from binary word images. The adopted features' extraction technique is the block-based Discrete Cosine Transform (DCT) applied to the whole word image. Feature vectors extracted using block-based DCT with non-overlapping sub-block of size 8x8 pixels. The applied HMMs to the task are mono-model discrete one-dimensional HMMs (Bakis Model). A balanced actual scanned and synthetic database of word-image has been constructed to ensure an even distribution of word samples. The Arabic words are typewritten in five fonts having a size 14 points in a plain style. The statistical language models and lexicon words are extracted from The Holy Qur‟an. The systems are applied on word images with no overlap between the training and testing datasets. The actual scanned database is used to evaluate the word recogniser. The synthetic database is a large amount of data acquired for a reliable training of sentence recognition systems. This word recogniser evaluated in mono-font and multi-font contexts. The two types of word recogniser have been used to achieve a final recognition accuracy of99.30% and 73.47% in mono-font and multi-font, respectively. The achieved average accuracy by the sentence recogniser is 67.24% improved to 78.35% on average when using 5-gram post-processing. The complexity and accuracy of the post-processing module are evaluated and found that 4-gram is more suitable than 5-gram; it is much faster at an average improvement of 76.89%

Near Field Communication Applications

Near Field Communication (NFC) is a short-range, low power contactless communication between NFC-enabled devices that are held in the closed proximity to each other. NFC technology has been moving rapidly from its initial application areas of mobile payment services and contactless ticketing to the diversity of new areas. Three specific NFC tags highlighted in the thesis have different structures in terms of memory, security and usage in different applications. NFC information tags exploit the data exchange format NDEF standardized by NFC Forum. NFC applications are rapidly stepping into novel and diverse application areas. Often they are deployed in combination with different devices and systems through their integrability and adaptability features. The diverse application areas where NFC tags and cards are used cover smart posters, contactless ticketing, keys and access control, library services, entertainment services, social network services, education, location based services, work force and retail management and healthcare. In designing different NFC applications, it is necessary to take into consideration different design issues such as to choosing the NFC tools and devices according to the technical requirements of the application, considering especially the memory, security and price factors as well as their relation to the purpose and usage of the final product. The security aspect of the NFC tags is remarkably important in selecting the proper NFC device. The race between hackers attacking and breaking the security systems of programmable high level products and manufacturers to produce reliable secure systems and products seems to never end. This has proven to be case, for example, for trying MIFARE Ultralight and DESFire MF3ICD40 tags. An important consideration of studying the different applications of NFC tags and cards during the thesis work was to understand the ubiquitous character of NFC technology.Lähitunnistus yhteys tekniikka (NFC) on lyhyen tähtäimen, pienitehoinen, kontaktiton yhteydenpito NFC yhteensopivien laitteiden välillä, jossa laitteet pidetään toistensä välittömässä läheisyydessä tiedon siirtämiseksi niiden välillä. NFC-teknologia on siirtynyt nopeasti sen alkuperäisiltä toimialueilta eli mobiili maksupalvelujen ja kontaktittomien lippujen sovellusalueilta moninaisille uusille alueille. Kolmella NFC tagillä, joita on käsitelty tässä tutkielmassa, on muistin, turvallisuuden ja käytön kannalta erilaisiä rakenteita, joita käytetään eri sovelluksissa. NFC-tagit käyttävät tiedonvälityksessä NFC Forumin standardoimaa NDEF-tiedonvaihtoformaattia. NFC sovellukset esiintyvät yhä enenevässä määrin nopeasti kehyttyvillä, uudenlaisilla ja monipuolisilla sovellusalueilla, usein yhdessä eri laitteiden ja järjestelmien kanssa. NFC on käytettävissä erinäisten laitteiden kanssa erilaisissa järjestelmäympäristöissä. Monipuoliset sovellusalueet, joissa muun muassa NFC-tagejä ja -kortteja käytetään sisältävät seuraavanlaisia sovelluksia: älykkäät julisteet, kontaktittomat liput, avaimet ja pääsynvalvonta, kirjastopalvelut, viihdepalvelut, sosiaalisen verkoston palvelut, kasvatukseen ja koulutukseen liittyvät palvelut, sijaintiperustaiset palvelut, työvoiman ja vähittäiskaupan hallinto-palvelut ja terveyspalvelut. Erilaisten NFC-sovelluksien suunnittelussa on väistämätöntä ottaa erilaisia suunnitteluasioita huomioon kuten valita NFC-työkalut ja laitteet sovelluksen teknisten vaatimusten mukaan. Erilaiset tärkeät tekijät kuten muisti, tietoturvallisuusominaisuudet ja hinta ja niiden kaikkien toimivuus lopputuotteen kannalta on otettava huomioon. Tietoturvallisuusnäkökohta on erityisen tärkeä oikean NFC laitteen valitsemisessa, sillä käynnissä on loputon kilpajuoksu hakkerien, jotka yrittävät rikkoa ohjelmoitavien korkeatasoisten laitteiden ja tuotteiden tietoturvajärjestelmiä, ja valmistajien, jotka pyrkivät tuottamaan luotettavia varmoja järjestelmiä, välillä. Tietoturvariskiin liittyviä ongelmia on löydetty esimerkiksi MIFARE Ultralight ja DESFire MF3ICD40 tageista. Tärkeä havainto, joka saatiin erilaisten NFC sovelluksien tutkimisesta, oli oivaltaa NFCteknologian potentiaalinen kaikkialle ulottuva, yleiskäyttöinen luonne

Cyber Security

This open access book constitutes the refereed proceedings of the 16th International Annual Conference on Cyber Security, CNCERT 2020, held in Beijing, China, in August 2020. The 17 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized according to the following topical sections: access control; cryptography; denial-of-service attacks; hardware security implementation; intrusion/anomaly detection and malware mitigation; social network security and privacy; systems security