Semantic Document Layout Analysis of Handwritten Manuscripts

A document layout can be more informative than merely a document’s visual and structural appearance.Thus,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives.This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts.The proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative labeling.This approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex layouts.The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts.It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.

Analysis of Adverse Reactions in the Treatment of COVID-19 with Three Chinese Patent Medicines and Three Herbal Formulas

Objective To explore the rules and characteristics of the adverse drug reactions(ADRs)of three Chinese patent medicines and three herbal formulas for the treatment of COVID-19,and to provide a reference for clinical safe medication.Methods The cases and ADR reports of the three Chinese patent medicines and three herbal formulas in PubMed,Web of Science,Springer Link,CNKI,Wanfang and VIP database were retrieved from December 2019 to May 2021.Then we extracted and analyzed the effective information included in the literature.Results and Conclusion According to the pre-developed retrieval plan,a total of 136 documents were obtained,and a total of 6 documents met the inclusion criteria finally.553 patients used three Chinese patent medicines and three herbal formulas,and there were 133 cases of adverse reactions.The adverse reactions of patients taking the three Chinese patent medicines and three herbal formulas can all be explained under the theory of traditional Chinese medicine,and the adverse reactions can be eliminated by adding or subtracting the flavor of the medicine or stopping the medicine.

The Origin Review and A Case Study of Plain Language in Federal Documents

Plain Language has made a great difference nowadays. As it turns out, Plain Language works effectively to express clearly, concisely and systematically. However, it is necessary for contemporary practitioners to review the origin and development of Plain Language Movement and to examine whether it has thoroughly implemented Plain Language policies in every federal document. Examining a contemporary federal document against the Guidelines for Document Designers reveals existing problems for further development.

Radon CLF:A Novel Approach for Skew Detection Using Radon Transform

In the digital world,a wide range of handwritten and printed documents should be converted to digital format using a variety of tools,including mobile phones and scanners.Unfortunately,this is not an optimal procedure,and the entire document image might be degraded.Imperfect conversion effects due to noise,motion blur,and skew distortion can lead to significant impact on the accuracy and effectiveness of document image segmentation and analysis in Optical Character Recognition(OCR)systems.In Document Image Analysis Systems(DIAS),skew estimation of images is a crucial step.In this paper,a novel,fast,and reliable skew detection algorithm based on the Radon Transform and Curve Length Fitness Function(CLF),so-called Radon CLF,was proposed.The Radon CLF model aims to take advantage of the properties of Radon spaces.The Radon CLF explores the dominating angle more effectively for a 1D signal than it does for a 2D input image due to an innovative fitness function formulation for a projected signal of the Radon space.Several significant performance indicators,including Mean Square Error(MSE),Mean Absolute Error(MAE),Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Measure(SSIM),Accuracy,and run-time,were taken into consideration when assessing the performance of our model.In addition,a new dataset named DSI5000 was constructed to assess the accuracy of the CLF model.Both two-dimensional image signal and the Radon space have been used in our simulations to compare the noise effect.Obtained results show that the proposed method is more effective than other approaches already in use,with an accuracy of roughly 99.87%and a run-time of 0.048(s).The introduced model is far more accurate and timeefficient than current approaches in detecting image skew.

Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

Cloud storage is essential for managing user data to store and retrieve from the distributed data centre.The storage service is distributed as pay a service for accessing the size to collect the data.Due to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy,duplication leads to increase storage space.The potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data analysis.It creates a complex nature to increase the storage consumption under cost.To resolve this problem,this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication(HIBD)based on Segmented Bind Linkage(SBL)Methods for reducing storage in a cloud environment.Initially,preprocessing is done using the sparse augmentation technique.Further,the preprocessed files are segmented into blocks to make Hash-Index.The block of the contents is compared with other files through Semantic Content Source Deduplication(SCSD),which identifies the similar content presence between the file.Based on the content presence count,the Distance Vector Weightage Correlation(DVWC)estimates the document similarity weight,and related files are grouped into a cluster.Finally,the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match case.This implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.

APPCorp:a corpus for Android privacy policy document structure analysis

With the increasing popularity of mobile devices and the wide adoption of mobile Apps,an increasing concern of privacy issues is raised.Privacy policy is identified as a proper medium to indicate the legal terms,such as the general data protection regulation(GDPR),and to bind legal agreement between service providers and users.However,privacy policies are usually long and vague for end users to read and understand.It is thus important to be able to automatically analyze the document structures of privacy policies to assist user understanding.In this work we create a manually labelled corpus containing 231 privacy policies(of more than 566,000 words and 7,748 annotated paragraphs).We benchmark our data corpus with 3 document classification models and achieve more than 82%on F1-score.

Document Analysis by Crosscount Approach

In this paper a new feature called crosscount for document analysis is introduced.The feature crosscount is a function of white line segment with its start on the edgeof document images. It reflects not only the contour of image, but also the periodicity of white lines(background) and text lines in the document images. In complexprinted-page layouts, there are different blocks such as textual, graphical, tabular, andso on. of these blocks, textual ones have the most obvious periodicity with their homogeneous white lines arranged regularly. The important property of textual blockscan be extracted by crosscount functions. Here the document layouts are classifiedinto three classes on the basis of their physical structures. Then the definition andproperties of the crosscount function are described. According to the classification ofdocument layouts, the application of this new feature to different types of documentimages analysis and understanding is discussed.

Visual Similarity Based Document Layout Analysis

In this paper, a visual similarity based document layout analysis （DLA） scheme is proposed, which by using clustering strategy can adaptively deal with documents in different languages, with different layout structures and skew angles. Aiming at a robust and adaptive DLA approach, the authors first manage to find a set of representative filters and statistics to characterize typical texture patterns in document images, which is through a visual similarity testing process. Texture features are then extracted from these filters and passed into a dynamic clustering procedure, which is called visual similarity clustering. Finally, text contents are located from the clustered results. Benefit from this scheme, the algorithm demonstrates strong robustness and adaptability in a wide variety of documents, which previous traditional DLA approaches do not possess.

AUTOMATIC PATENT DOCUMFNT SUMMARIZATION FOR COLLABORATIVE KNOWLEDGE SYSTEMS AND SERVICES

Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R＆D knowledge, reduces new product development time, increases market success, and reduces potential patent infringement. Thus, it is beneficial to automatically and systematically extract information from patent documents in order to improve knowledge sharing and collaboration among R＆D team members. In this research, patents are summarized using a combined ontology based and TF-IDF concept clustering approach. The ontology captures the general knowledge and core meaning of patents in a given domain. Then, the proposed methodology extracts, clusters, and integrates the content of a patent to derive a summary and a cluster tree diagram of key terms. Patents from the International Patent Classification （IPC） codes B25C, B25D, B25F （categories for power hand tools） and B24B, C09G and H011 （categories for chemical mechanical polishing） are used as case studies to evaluate the compression ratio, retention ratio, and classification accuracy of the summarization results. The evaluation uses statistics to represent the summary generation and its compression ratio, the ontology based keyword extraction retention ratio, and the summary classification accuracy. The results show that the ontology based approach yields about the same compression ratio as previous non-ontology based research but yields on average an 11% improvement for the retention ratio and a 14% improvement for classification accuracy.

Measuring Similarity of Academic Articles with Semantic Profile and Joint Word Embedding

Long-document semantic measurement has great significance in many applications such as semantic searchs, plagiarism detection, and automatic technical surveys. However, research efforts have mainly focused on the semantic similarity of short texts. Document-level semantic measurement remains an open issue due to problems such as the omission of background knowledge and topic transition. In this paper, we propose a novel semantic matching method for long documents in the academic domain. To accurately represent the general meaning of an academic article, we construct a semantic profile in which key semantic elements such as the research purpose, methodology, and domain are included and enriched. As such, we can obtain the overall semantic similarity of two papers by computing the distance between their profiles. The distances between the concepts of two different semantic profiles are measured by word vectors. To improve the semantic representation quality of word vectors, we propose a joint word-embedding model for incorporating a domain-specific semantic relation constraint into the traditional context constraint. Our experimental results demonstrate that, in the measurement of document semantic similarity, our approach achieves substantial improvement over state-of-the-art methods, and our joint word-embedding model produces significantly better word representations than traditional word-embedding models.

Arabic Bank Check Processing: State of the Art

In this paper, we present a general model for Arabic bank check processing indicating the major phases of a check processing system. We then survey the available databases for Arabic bank check processing research. The state of the art in the different phases of Arabic bank check processing is surveyed （i.e., pre-processing, check analysis and segmentation, features extraction, and legal and courtesy amounts recognition）. The open issues for future research are stated and areas that need improvements are presented. To the best of our knowledge, it is the first survey of Arabic bank check processing.

Self-identification of electronically scanned signatures(ESS)and digitally constructed signatures(DCS)

The use of electronic signatures as a form of identification is increasingly common,yet they have been shown to lack the dynamic features found in online signatures.In this study,handwritten signatures were scanned to produce electronically scanned signatures(ESS)which were then digitally altered to produce digitally constructed signatures(DCS).The ESS and DCS were presented back to participants to identify which were genuine.Only 1%of participants correctly identified all signatures,with a mean score of 57.6%identifications.The lack of self-recognition of ESS raises questions on their reliability and usefulness as means of personal identification.

Extended Approach to Water Flow Algorithm for Text Line Segmentation

This paper proposes a new approach to the water flow algorithm for text line segmentation. In the basic method the hypothetical water flows under few specified angles which have been defined by water flow angle as parameter. It is applied to the document image frame from left to right and vice versa. As a result, the unwetted and wetted areas are established. These areas separate text from non-text elements in each text line, respectively. Hence, they represent the control areas that are of major importance for text line segmentation. Primarily, an extended approach means extraction of the connected-components by bounding boxes over text. By this way, each connected component is mutually separated. Hence, the water flow angle, which defines the unwetted areas, is determined adaptively. By choosing appropriate water flow angle, the unwetted areas are lengthening which leads to the better text line segmentation. Results of this approach are encouraging due to the text line segmentation improvement which is the most challenging step in document image processing.

Knowledge-driven decision analytics for commercial banking

Although the corporate relationship manager seems to be the key enabler in commercial banking,the personal relationship sales model is not a sustainable model for the paradigm shift in digital financial markets.In this research,we propose a knowledge-driven decision analytics approach to improve the decision process.However,most of the corporate client documents processed in banks are not well-structured and the traditional analysis approach does not consider the document structure,which carries rich semantic information.We propose a document structure-based text representation approach with incorporating auxiliary information in the predictive analytics of unstructured data to improve the performance in the document classification task.The proposed approach significantly outperforms the traditional whole document approach which does not take into considerations of the document structure.With the proposed approach,knowledge can be effectively and efficiently used for business decisions and planning to improve the competitive advantage and substantiality of banks.

Purposeful and Ethical Early Childhood Teacher:The Underlying Values Guiding Finnish Early Childhood Education

Purpose:The new Finnish National Core Curriculum for Early Childhood Education and Care(2018)strongly highlights pedagogical knowledge and practice,demanding teachers to develop their pedagogical thinking,evaluation,judgment,and operating culture.Since ethics is viewed as vital characteristics of the teaching profession,our objective is to make these complex ethical issues more visible to be subject to democratic discussion and change.Design/Approach/Methods:The framework comprises a broad theory base of codes of ethics and professional codes of ethics of teaching.The research materials were national curricula of early childhood education and care(ECEC)-and pre-primary education.The eight-step qualitative analysis process was applied to identify and shed light on the codes of ethics laying the foundations forpurposeful and ethical earlychildhood education(ECE)teacher.Findings:The results indicate that through both theoretical lenses,the Finnish ECEC curricula comprise several ethical codes.For the future purposeful ECE teachers as ethical professionals,the results raise questions for further discussion.Particularly,issues related to the ethics of care,intellectual freedom,inquiry stance,and professional competence,and diversity may further enhance our ECEC curricula.