221 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    The mad manifesto

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    The “mad manifesto” project is a multidisciplinary mediated investigation into the circumstances by which mad (mentally ill, neurodivergent) or disabled (disclosed, undisclosed) students faced far more precarious circumstances with inadequate support models while attending North American universities during the pandemic teaching era (2020-2023). Using a combination of “emergency remote teaching” archival materials such as national student datasets, universal design for learning (UDL) training models, digital classroom teaching experiments, university budgetary releases, educational technology coursewares, and lived experience expertise, this dissertation carefully retells the story of “accessibility” as it transpired in disabling classroom containers trapped within intentionally underprepared crisis superstructures. Using rhetorical models derived from critical disability studies, mad studies, social work practice, and health humanities, it then suggests radically collaborative UDL teaching practices that may better pre-empt the dynamic needs of dis/abled students whose needs remain direly underserviced. The manifesto leaves the reader with discrete calls to action that foster more critical performances of intersectionally inclusive UDL classrooms for North American mad students, which it calls “mad-positive” facilitation techniques: 1. Seek to untie the bond that regards the digital divide and access as synonyms. 2. UDL practice requires an environment shift that prioritizes change potential. 3. Advocate against the usage of UDL as a for-all keystone of accessibility. 4. Refuse or reduce the use of technologies whose primary mandate is dataveillance. 5. Remind students and allies that university space is a non-neutral affective container. 6. Operationalize the tracking of student suicides on your home campus. 7. Seek out physical & affectual ways that your campus is harming social capital potential. 8. Revise policies and practices that are ability-adjacent imaginings of access. 9. Eliminate sanist and neuroscientific languaging from how you speak about students. 10. Vigilantly interrogate how “normal” and “belong” are socially constructed. 11. Treat lived experience expertise as a gift, not a resource to mine and to spend. 12. Create non-psychiatric routes of receiving accommodation requests in your classroom. 13. Seek out uncomfortable stories of mad exclusion and consider carceral logic’s role in it. 14. Center madness in inclusive methodologies designed to explicitly resist carceral logics. 15. Create counteraffectual classrooms that anticipate and interrupt kairotic spatial power. 16. Strive to refuse comfort and immediate intelligibility as mandatory classroom presences. 17. Create pathways that empower cozy space understandings of classroom practice. 18. Vector students wherever possible as dynamic ability constellations in assessment

    An end-to-end, interactive Deep Learning based Annotation system for cursive and print English handwritten text

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    With the surging inclination towards carrying out tasks on computational devices and digital mediums, any method that converts a task that was previously carried out manually, to a digitized version, is always welcome. Irrespective of the various documentation tasks that can be done online today, there are still many applications and domains where handwritten text is inevitable, which makes the digitization of handwritten documents a very essential task. Over the past decades, there has been extensive research on offline handwritten text recognition. In the recent past, most of these attempts have shifted to Machine learning and Deep learning based approaches. In order to design more complex and deeper networks, and ensure stellar performances, it is essential to have larger quantities of annotated data. Most of the databases present for offline handwritten text recognition today, have either been manually annotated or semi automatically annotated with a lot of manual involvement. These processes are very time consuming and prone to human errors. To tackle this problem, we present an innovative, complete end-to-end pipeline, that annotates offline handwritten manuscripts written in both print and cursive English, using Deep Learning and User Interaction techniques. This novel method, which involves an architectural combination of a detection system built upon a state-of-the-art text detection model, and a custom made Deep Learning model for the recognition system, is combined with an easy-to-use interactive interface, aiming to improve the accuracy of the detection, segmentation, serialization and recognition phases, in order to ensure high quality annotated data with minimal human interaction.Comment: 17 pages, 8 figures, 2 table

    UTRNet: High-Resolution Urdu Text Recognition In Printed Documents

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    In this paper, we propose a novel approach to address the challenges of printed Urdu text recognition using high-resolution, multi-scale semantic feature extraction. Our proposed UTRNet architecture, a hybrid CNN-RNN model, demonstrates state-of-the-art performance on benchmark datasets. To address the limitations of previous works, which struggle to generalize to the intricacies of the Urdu script and the lack of sufficient annotated real-world data, we have introduced the UTRSet-Real, a large-scale annotated real-world dataset comprising over 11,000 lines and UTRSet-Synth, a synthetic dataset with 20,000 lines closely resembling real-world and made corrections to the ground truth of the existing IIITH dataset, making it a more reliable resource for future research. We also provide UrduDoc, a benchmark dataset for Urdu text line detection in scanned documents. Additionally, we have developed an online tool for end-to-end Urdu OCR from printed documents by integrating UTRNet with a text detection model. Our work not only addresses the current limitations of Urdu OCR but also paves the way for future research in this area and facilitates the continued advancement of Urdu OCR technology. The project page with source code, datasets, annotations, trained models, and online tool is available at abdur75648.github.io/UTRNet.Comment: Accepted at The 17th International Conference on Document Analysis and Recognition (ICDAR 2023

    24th Nordic Conference on Computational Linguistics (NoDaLiDa)

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    Multi-Granularity Prediction with Learnable Fusion for Scene Text Recognition

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    Due to the enormous technical challenges and wide range of applications, scene text recognition (STR) has been an active research topic in computer vision for years. To tackle this tough problem, numerous innovative methods have been successively proposed, and incorporating linguistic knowledge into STR models has recently become a prominent trend. In this work, we first draw inspiration from the recent progress in Vision Transformer (ViT) to construct a conceptually simple yet functionally powerful vision STR model, which is built upon ViT and a tailored Adaptive Addressing and Aggregation (A3^3) module. It already outperforms most previous state-of-the-art models for scene text recognition, including both pure vision models and language-augmented methods. To integrate linguistic knowledge, we further propose a Multi-Granularity Prediction strategy to inject information from the language modality into the model in an implicit way, \ie, subword representations (BPE and WordPiece) widely used in NLP are introduced into the output space, in addition to the conventional character level representation, while no independent language model (LM) is adopted. To produce the final recognition results, two strategies for effectively fusing the multi-granularity predictions are devised. The resultant algorithm (termed MGP-STR) is able to push the performance envelope of STR to an even higher level. Specifically, MGP-STR achieves an average recognition accuracy of 94%94\% on standard benchmarks for scene text recognition. Moreover, it also achieves state-of-the-art results on widely-used handwritten benchmarks as well as more challenging scene text datasets, demonstrating the generality of the proposed MGP-STR algorithm. The source code and models will be available at: \url{https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/MGP-STR}.Comment: submitted to TPAMI; an extension to our previous ECCV 2022 paper arXiv:2209.0359

    Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics

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    Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional integration methods. Here, methods (1) and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3) relying on convolutional neural networks. Pure ML methods to solve (nonlinear) PDEs are represented by Physics-Informed Neural network (PINN) methods, which could be combined with attention mechanism to address discontinuous solutions. Both LSTM and attention architectures, together with modern and generalized classic optimizers to include stochasticity for DL networks, are extensively reviewed. Kernel machines, including Gaussian processes, are provided to sufficient depth for more advanced works such as shallow networks with infinite width. Not only addressing experts, readers are assumed familiar with computational mechanics, but not with DL, whose concepts and applications are built up from the basics, aiming at bringing first-time learners quickly to the forefront of research. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics, even in well-known references. Positioning and pointing control of a large-deformable beam is given as an example.Comment: 275 pages, 158 figures. Appeared online on 2023.03.01 at CMES-Computer Modeling in Engineering & Science

    Leveraging Model Fusion for Improved License Plate Recognition

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    License Plate Recognition (LPR) plays a critical role in various applications, such as toll collection, parking management, and traffic law enforcement. Although LPR has witnessed significant advancements through the development of deep learning, there has been a noticeable lack of studies exploring the potential improvements in results by fusing the outputs from multiple recognition models. This research aims to fill this gap by investigating the combination of up to 12 different models using straightforward approaches, such as selecting the most confident prediction or employing majority vote-based strategies. Our experiments encompass a wide range of datasets, revealing substantial benefits of fusion approaches in both intra- and cross-dataset setups. Essentially, fusing multiple models reduces considerably the likelihood of obtaining subpar performance on a particular dataset/scenario. We also found that combining models based on their speed is an appealing approach. Specifically, for applications where the recognition task can tolerate some additional time, though not excessively, an effective strategy is to combine 4-6 models. These models may not be the most accurate individually, but their fusion strikes an optimal balance between accuracy and speed.Comment: Accepted for presentation at the Iberoamerican Congress on Pattern Recognition (CIARP) 202

    Customized mask region based convolutional neural networks for un-uniformed shape text detection and text recognition

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    In image scene, text contains high-level of important information that helps to analyze and consider the particular environment. In this paper, we adapt image mask and original identification of the mask region based convolutional neural networks (R-CNN) to allow recognition at 3 levels such as sequence, holistic and pixel-level semantics. Particularly, pixel and holistic level semantics can be utilized to recognize the texts and define the text shapes, respectively. Precisely, in mask and detection, we segment and recognize both character and word instances. Furthermore, we implement text detection through the outcome of instance segmentation on 2-D feature-space. Also, to tackle and identify the text issues of smaller and blurry texts, we consider text recognition by attention-based of optical character recognition (OCR) model with the mask R-CNN at sequential level. The OCR module is used to estimate character sequence through feature maps of the word instances in sequence to sequence. Finally, we proposed a fine-grained learning technique that trains a more accurate and robust model by learning models from the annotated datasets at the word level. Our proposed approach is evaluated on popular benchmark dataset ICDAR 2013 and ICDAR 2015
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