Information scraps: how and why information eludes our personal information management tools

In this paper we describe information scraps -- a class of personal information whose content is scribbled on Post-it notes, scrawled on corners of random sheets of paper, buried inside the bodies of e-mail messages sent to ourselves, or typed haphazardly into text files. Information scraps hold our great ideas, sketches, notes, reminders, driving directions, and even our poetry. We define information scraps to be the body of personal information that is held outside of its natural or We have much still to learn about these loose forms of information capture. Why are they so often held outside of our traditional PIM locations and instead on Post-its or in text files? Why must we sometimes go around our traditional PIM applications to hold on to our scraps, such as by e-mailing ourselves? What are information scraps' role in the larger space of personal information management, and what do they uniquely offer that we find so appealing? If these unorganized bits truly indicate the failure of our PIM tools, how might we begin to build better tools? We have pursued these questions by undertaking a study of 27 knowledge workers. In our findings we describe information scraps from several angles: their content, their location, and the factors that lead to their use, which we identify as ease of capture, flexibility of content and organization, and avilability at the time of need. We also consider the personal emotive responses around scrap management. We present a set of design considerations that we have derived from the analysis of our study results. We present our work on an application platform, jourknow, to test some of these design and usability findings

Content Detection in Handwritten Documents

abstract: Handwritten documents have gained popularity in various domains including education and business. A key task in analyzing a complex document is to distinguish between various content types such as text, math, graphics, tables and so on. For example, one such aspect could be a region on the document with a mathematical expression; in this case, the label would be math. This differentiation facilitates the performance of specific recognition tasks depending on the content type. We hypothesize that the recognition accuracy of the subsequent tasks such as textual, math, and shape recognition will increase, further leading to a better analysis of the document. Content detection on handwritten documents assigns a particular class to a homogeneous portion of the document. To complete this task, a set of handwritten solutions was digitally collected from middle school students located in two different geographical regions in 2017 and 2018. This research discusses the methods to collect, pre-process and detect content type in the collected handwritten documents. A total of 4049 documents were extracted in the form of image, and json format; and were labelled using an object labelling software with tags being text, math, diagram, cross out, table, graph, tick mark, arrow, and doodle. The labelled images were fed to the Tensorflow’s object detection API to learn a neural network model. We show our results from two neural networks models, Faster Region-based Convolutional Neural Network (Faster R-CNN) and Single Shot detection model (SSD).Dissertation/ThesisMasters Thesis Computer Science 201

Detecting Authorship, Hands, and Corrections in Historical Manuscripts. A Mixedmethods Approach towards the Unpublished Writings of an 18th Century Czech Emigré Community in Berlin (Handwriting)

When one starts working philologically with historical manuscripts, one faces important first questions involving authorship, writers’ hands andthe history of documenttransmission. These issues are especially thorny with documents remaining outside the established canon, such as privatemanuscripts, aboutwhichwehave very restrictedtext-externalinformation. In this area – so we argue – it is especially fruitful to employ a mixed-methods approach, combiningtailored automatic methods from image recognition/analysis with philological and linguistic knowledge.Whileimage analysis captureswriters’ hands, linguistic/philological research mainly addressestextual authorship;thetwo cross-fertilize and obtain a coherent interpretation which may then be evaluated against the available text-external historical evidence. Departingfrom our ‘lab case’,whichis a corpus of unedited Czechmanuscriptsfromthe archive of a small 18th century migrant community, the Herrnhuter Brüdergemeine (Brethren parish) in Berlin-Neukölln, our project has developed an assistance system which aids philologists in working with digitized (scanned) hand-written historical sources. We present its application and discuss its general potential and methodological implications

The T-PEN Tool: Sustainability and Quality Control in Encoding Handwritten Texts

The Center for Digital Theology and the Carolingian Canon Law (CCL) Project will collaborate to develop a Transcription-Paleographic and Editing Notation (T-PEN) Tool. Using digitized images of medieval manuscripts, this tool will assist scholars in the transcription of such sources and will allow automatic insertion of markup of textual features during the transcription process. T-PEN will be a new research tool in the CCL, permitting any number of scholars to submit their transcriptions of unpublished texts of early medieval canon law. Moreover, T-PEN will assist the CCL in maintaining quality control of transcriptions, as it will supply continuous comparison of manuscript image and the transcriber's work in a transparent display. It supports easy and immediate insertion of annotations (paleographic, discursive, and bibliographic), insertion of TEI P5 markup according to project protocols and validation against the schema during the transcription process

Design of an Offline Handwriting Recognition System Tested on the Bangla and Korean Scripts

This dissertation presents a flexible and robust offline handwriting recognition system which is tested on the Bangla and Korean scripts. Offline handwriting recognition is one of the most challenging and yet to be solved problems in machine learning. While a few popular scripts (like Latin) have received a lot of attention, many other widely used scripts (like Bangla) have seen very little progress. Features such as connectedness and vowels structured as diacritics make it a challenging script to recognize. A simple and robust design for offline recognition is presented which not only works reliably, but also can be used for almost any alphabetic writing system. The framework has been rigorously tested for Bangla and demonstrated how it can be transformed to apply to other scripts through experiments on the Korean script whose two-dimensional arrangement of characters makes it a challenge to recognize. The base of this design is a character spotting network which detects the location of different script elements (such as characters, diacritics) from an unsegmented word image. A transcript is formed from the detected classes based on their corresponding location information. This is the first reported lexicon-free offline recognition system for Bangla and achieves a Character Recognition Accuracy (CRA) of 94.8%. This is also one of the most flexible architectures ever presented. Recognition of Korean was achieved with a 91.2% CRA. Also, a powerful technique of autonomous tagging was developed which can drastically reduce the effort of preparing a dataset for any script. The combination of the character spotting method and the autonomous tagging brings the entire offline recognition problem very close to a singular solution. Additionally, a database named the Boise State Bangla Handwriting Dataset was developed. This is one of the richest offline datasets currently available for Bangla and this has been made publicly accessible to accelerate the research progress. Many other tools were developed and experiments were conducted to more rigorously validate this framework by evaluating the method against external datasets (CMATERdb 1.1.1, Indic Word Dataset and REID2019: Early Indian Printed Documents). Offline handwriting recognition is an extremely promising technology and the outcome of this research moves the field significantly ahead

Restoration of deteriorated text sections in ancient document images using atri-level semi-adaptive thresholding technique

The proposed research aims to restore deteriorated text sections that are affected by stain markings, ink seepages and document ageing in ancient document photographs, as these challenges confront document enhancement. A tri-level semi-adaptive thresholding technique is developed in this paper to overcome the issues. The primary focus, however, is on removing deteriorations that obscure text sections. The proposed algorithm includes three levels of degradation removal as well as pre- and post-enhancement processes. In level-wise degradation removal, a global thresholding approach is used, whereas, pseudo-colouring uses local thresholding procedures. Experiments on palm leaf and DIBCO document photos reveal a decent performance in removing ink/oil stains whilst retaining obscured text sections. In DIBCO and palm leaf datasets, our system also showed its efficacy in removing common deteriorations such as uneven illumination, show throughs, discolouration and writing marks. The proposed technique directly correlates to other thresholding-based benchmark techniques producing average F-measure and precision of 65.73 and 93% towards DIBCO datasets and 55.24 and 94% towards palm leaf datasets. Subjective analysis shows the robustness of proposed model towards the removal of stains degradations with a qualitative score of 3 towards 45% of samples indicating degradation removal with fairly readable text

Handwritten Word Spotting with Corrected Attributes

International audienceWe propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results

Student and Instructor Responses to E-Feedback

This empirical study provides further insight into how instructors decide on the methods used to respond to student writing and whether these criteria match what students want from this feedback. What are instructors\u27 considerations when they adopt e-feedback practices? Do these considerations align or conflict with student preferences for how they receive feedback? How does the rhetorical content of these technologies (visual presentation and choices offered to users) affect the ways both teachers and students use them? To address a research gap, this study focuses on e-feedback, which is in-document feedback from instructors distributed via the Web to students (also called digital feedback or electronic feedback) in two of the formats most widely used by college composition instructors: comments added to Microsoft Word files and comments left in GradeMark, the essay annotation and grading platform embedded within Turnitin.com. By comparing through surveys student impressions of e-feedback with the criteria instructors use to make decisions about what technologies to use when commenting, my goal is to give teachers a tool to make better-informed choices