221 research outputs found
UMSL Bulletin 2023-2024
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
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
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
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
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
Multi-Granularity Prediction with Learnable Fusion for Scene Text Recognition
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 (A) 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 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
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
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
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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