High Accuracy Determination of Rheological Properties of Drilling Fluids Using the Marsh Funnel

Efficient and safe drilling operations require precise determination of rheological properties in drilling fluids, encompassing dynamic viscosity for Newtonian fluids, and apparent viscosity, plastic viscosity, and yield point for non-Newtonian fluids. Conventional viscometers like vibrating wire, ZNN-D6, and Fann-35 offer high accuracy but are limited by cost and complexity in small-scale industries and labs. To address this, our research presents a novel mathematical model based on the Herschel-Bulkley model, aiming to accurately characterise drilling fluids' rheological properties using the Marsh funnel as an alternative device -- an economical, operator-friendly, and power-independent equipment. Drawing inspiration from seminal works by Li et al. (2020), Sedaghat (2017), and Guria et al. (2013), this innovative framework establishes a universal inverse linear relationship between a fluid's flow factor and final discharge time. For any fluid, it utilises its density and flow factor (or final discharge time) to determine all its rheological properties. Specifically, it evaluates dynamic viscosity for Newtonian fluids, apparent viscosity, plastic viscosity, and yield point for weighted non-Newtonian fluids, and apparent viscosity for non-weighted non-Newtonian fluids, with average systematic errors (against Fann-35 measurements) of 0.39%, 3.52%, 2.17%, 18.38%, and 5.84%, respectively, surpassing the precision of alternative mathematical models found in the aforementioned literature. Furthermore, while our framework's precision in plastic viscosity and yield point assessment of non-weighted non-Newtonian fluids slightly lags behind the framework of Li et al. (2020), it outperforms the model of Sedaghat (2017). In conclusion, despite minor limitations, our proposed mathematical model holds huge promise for drilling fluid rheology in petroleum, drilling, and related industries.Comment: 57 pages, 1 figure, and 10 tables. Funding for this research work was provided through the IIChE Research Grant for the academic year 2022-23, granted by the Indian Institute of Chemical Engineers (IIChE

Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes

When used in a real-world noisy environment, the capacity to generalize to multiple domains is essential for any autonomous scene text spotting system. However, existing state-of-the-art methods employ pretraining and fine-tuning strategies on natural scene datasets, which do not exploit the feature interaction across other complex domains. In this work, we explore and investigate the problem of domain-agnostic scene text spotting, i.e., training a model on multi-domain source data such that it can directly generalize to target domains rather than being specialized for a specific domain or scenario. In this regard, we present the community a text spotting validation benchmark called Under-Water Text (UWT) for noisy underwater scenes to establish an important case study. Moreover, we also design an efficient super-resolution based end-to-end transformer baseline called DA-TextSpotter which achieves comparable or superior performance over existing text spotting architectures for both regular and arbitrary-shaped scene text spotting benchmarks in terms of both accuracy and model efficiency. The dataset, code and pre-trained models will be released upon acceptance.Comment: 10 image

SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation

Instance-level segmentation of documents consists in assigning a class-aware and instance-aware label to each pixel of the image. It is a key step in document parsing for their understanding. In this paper, we present a unified transformer encoder-decoder architecture for en-to-end instance segmentation of complex layouts in document images. The method adapts a contrastive training with a mixed query selection for anchor initialization in the decoder. Later on, it performs a dot product between the obtained query embeddings and the pixel embedding map (coming from the encoder) for semantic reasoning. Extensive experimentation on competitive benchmarks like PubLayNet, PRIMA, Historical Japanese (HJ), and TableBank demonstrate that our model with SwinL backbone achieves better segmentation performance than the existing state-of-the-art approaches with the average precision of \textbf{93.72}, \textbf{54.39}, \textbf{84.65} and \textbf{98.04} respectively under one billion parameters. The code is made publicly available at: \href{https://github.com/ayanban011/SwinDocSegmenter}{github.com/ayanban011/SwinDocSegmenter}Comment: Accepted to ICDAR 2023 (San Jose, California

Beyond Document Page Classification: Design, Datasets, and Challenges

This paper highlights the need to bring document classification benchmarking closer to real-world applications, both in the nature of data tested ($X$: multi-channel, multi-paged, multi-industry; $Y$: class distributions and label set variety) and in classification tasks considered ($f$: multi-page document, page stream, and document bundle classification, ...). We identify the lack of public multi-page document classification datasets, formalize different classification tasks arising in application scenarios, and motivate the value of targeting efficient multi-page document representations. An experimental study on proposed multi-page document classification datasets demonstrates that current benchmarks have become irrelevant and need to be updated to evaluate complete documents, as they naturally occur in practice. This reality check also calls for more mature evaluation methodologies, covering calibration evaluation, inference complexity (time-memory), and a range of realistic distribution shifts (e.g., born-digital vs. scanning noise, shifting page order). Our study ends on a hopeful note by recommending concrete avenues for future improvements.}Comment: 8 pages, under revie

SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation

Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc. However, most of the existing works have ignored the crucial fact regarding the scarcity of labeled data. With growing internet connectivity to personal life, an enormous amount of documents had been available in the public domain and thus making data annotation a tedious task. We address this challenge using self-supervision and unlike, the few existing self-supervised document segmentation approaches which use text mining and textual labels, we use a complete vision-based approach in pre-training without any ground-truth label or its derivative. Instead, we generate pseudo-layouts from the document images to pre-train an image encoder to learn the document object representation and localization in a self-supervised framework before fine-tuning it with an object detection model. We show that our pipeline sets a new benchmark in this context and performs at par with the existing methods and the supervised counterparts, if not outperforms. The code is made publicly available at: https://github.com/MaitySubhajit/SelfDocSegComment: Accepted at The 17th International Conference on Document Analysis and Recognition (ICDAR 2023

Text-DIAE: A Self-Supervised Degradation Invariant Autoencoder for Text Recognition and Document Enhancement

In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement. We start by employing a transformer-based architecture that incorporates three pretext tasks as learning objectives to be optimized during pre-training without the usage of labelled data. Each of the pretext objectives is specifically tailored for the final downstream tasks. We conduct several ablation experiments that confirm the design choice of the selected pretext tasks. Importantly, the proposed model does not exhibit limitations of previous state-of-the-art methods based on contrastive losses, while at the same time requiring substantially fewer data samples to converge. Finally, we demonstrate that our method surpasses the state-of-the-art in existing supervised and self-supervised settings in handwritten and scene text recognition and document image enhancement. Our code and trained models will be made publicly available at https://github.com/dali92002/SSL-OC