A survey of document image word spotting techniques

The rapid growth of digital libraries, e-governance, and internet based applications has caused an exponential escalation in the volume of ‘Big-data’ particularly due to texts, images, audios and videos that are being both archived and transmitted on a daily basis. In order to make their storage and transfer efficient, different data compression techniques are used in the literature. The ultimate motive behind data compression is to transform a big size data into small size data, which eventually implies less space while archiving, and less time in transferring. However, in order to operate/analyze compressed data, it is usually necessary to decompress it, so as to bring back the data to its original form, which unfortunately warrants an additional computing cost. In this backdrop, if operating upon the compressed data itself can be made possible without going through the stage of decompression, then the advantage that could be accomplished due to compression would escalate. Further due to compression, from the data structure and storage perspectives, the original visibility structure of the data also being lost, it turns into a potential challenge to trace the original information in the compressed representation. This challenge is the motivation behind exploring the idea of direct processing on the compressed data itself in the literature. The proposed survey paper specifically focuses on compressed document images and brings out two original contributions. The first contribution is that it presents a critical study on different image analysis and image compression techniques, and highlights the motivational reasons for pursuing document image analysis in the compressed domain. The second contribution is that it summarizes the different compressed domain techniques in the literature so far based on the type of compression and operations performed by them. Overall, the paper aims to provide a perspective for pursuing further research in the area of document image analysis and pattern recognition directly based on the compressed data.

A review on document image analysis techniques directly in the compressed domain

Cloud computing technologies offer major benefits to the IT industries in terms of elasticity and rapid provisioning, pay-as-you-go-model, reduced capital cost, access to unlimited resources, flexibility. Job scheduling is a combinatorial optimization problem in the fields of computer science where the ideal jobs are assigned to required resource at a particular instant of time. In this paper we proposed Hybrid algorithm which combine the advantage of ACO and Cuckoo search. The makespan or completion time can be reduced with the help of hybrid algorithm, since the jobs have been executed with in the specified time interval by allocation of required resources using the Hybrid algorithm. The obtain results shows that Hybrid algorithm performs well than compared with the ACO algorithm in terms of performance of the algorithm and makespan.

Minimizing the makespan using Hybrid algorithm for cloud computing

Pattern Matching in Compressed Texts and Images surveys and appraises techniques for pattern matching in compressed text and images. Normally compressed data needs to be decompressed before it is processed. If however the compression has been done in the right way, it is often possible to search the data without having to decompress it, or, at least, only partially decompress it. The problem can be divided into lossless and lossy compression methods, and then in each of these cases the pattern matching can be either exact or inexact. Much work has been reported in the literature on techniques for all of these cases. It includes algorithms that are suitable for pattern matching for various compression methods, and compression methods designed specifically for pattern matching. This monograph provides a survey of this work while also identifying the important relationship between pattern matching and compression, and proposing some performance measures for compressed pattern matching algorithms. Pattern Matching in Compressed Texts and Images is an excellent reference text for anyone who has an interest in the problem of searching compressed text and images. It concludes with a particularly insightful section on the ideas and research directions that are likely to occupy researchers in this field in the short and long term.

/pdf/pattern-matching-in-compressed-texts-and-images-4fz2d7bsn3.pdf

Pattern Matching in Compressed Texts and Images

In this paper, a two-step segmentation-free word spotting method for historical printed documents is presented. The first step involves a minimum distance matching between a query keyword image and a document page image using keypoint correspondences. In the second step of the method, the matched keypoints on the document image serve as indicators for creating candidate image areas. The query keyword image is matched against the candidate image areas in order to properly estimate the bounding boxes of the detected word instances. The method is evaluated using two datasets of different languages and is compared against segmentation-free state-of-the-art methods. The experimental results show that the proposed method outperforms significantly the competitive approaches.

A segmentation-free word spotting method for historical printed documents

This paper attempts to provide a survey of the past researches on character based as keyword based approaches used for retrieving information from document images. This survey also provides insights into the strengths and weaknesses of current techniques, relevancy lies between each technique and also the guidance in choosing the area that future work on document image retrieval could address.

A survey of keyword spotting techniques for printed document images

Cognitive computing technological trends and future research directions in healthcare - A systematic literature review

Suicidal intention or ideation detection is one of the evolving research fields in social media. People use this platform to share their thoughts, tendencies, opinions, and feelings toward suicide. Therefore, this task becomes a challenging one due to the unstructured and noisy texts. In this paper, we propose five BERT-based pre-trained transformer models, namely, BERT, DistilBERT, ALBERT, RoBERTa, and DistilRoBERTa, for the task of suicidal intention detection. The performance of these models evaluated using the standard classification metrics. Specifically, we use the one-cycle learning rate policy to train all models. Our results show that the RoBERTa model achieves a better performance than other BERT-based models. The model gains 99.23%, 96.35%, and 95.39% accuracy for training, validation, and testing, respectively.

Suicidal Intention Detection in Tweets Using BERT-Based Transformers

A Hybrid Ai Framework for Multi-Label Toxicity Category Detection

In recent days, the research on student’s intelligence level modelling is a challenging Artificial Intelligence (AI) task, which gains more attraction because it provides actionable insights to the tutor by analyzing the intelligence level of the learners. Each learner’s knowledge, comprehension, and intellectual capacities are unique. It is critical to identify these capacities and provide learners, particularly slow learners, with the necessary knowledge. Cognitive Performance Test (CPT) is an essential component for assessing the knowledge level of students. The reasoning level or coefficient deals with the analysis of the thinking capability in a logical way. It also reflects the child’s learning potential. The main aim of the proposed system is to design a Cognitive Knowledge Representation Model (CKRM), which fuses Cognitive Performance Metrics (CPM) calculation and Reasoning Coefficient Calculation (RCC) algorithms to assess the student’s intelligence level. The result of the proposed system is stratification of students to three different ranges of reasoning coefficient. The CKRM consists of the following phases: data collection, statistical Exploratory Data Analysis (EDA), model building and analysis, which involve the assessment of the knowledge level using CPT and calculation of reasoning coefficient using First Order Logic (FOL), and finally model evaluation using cognitive evaluation metrics. CPM and RCC algorithms have been proposed in this paper to calculate the student’s reasoning coefficient by using the forward chaining FOL inference engine. The dataset is a real time data which consists of the academic and cognitive performance details of school students from classes 1 to 6 for the year 2019 to 2020. The academic data are collected from the Educational Management Information System (EMIS) maintained by the school. The cognitive performance data are collected by conducting the tests for the students using the memory training application called Lumosity. The proposed system’s performance is evaluated using ten Machine Learning (ML) algorithms in which the Quadratic Discriminant Analysis achieved an accuracy of 0.97 for classes 1, 2, and 3. For classes 4, 5, and 6, nearly twelve ML algorithms are evaluated in which Random Forest (RF) Classifier achieved an accuracy of 0.98. Six math expert committee teachers concluded that the reasoning coefficient value was acceptable with an average accuracy of 0.92 for classes 1, 2, 3 and 0.9 for classes 4, 5, 6. In comparison to the pre-existing models employed in the prior research, it was determined that the created CKRM (academic and cognitive) was superior. The cognitive metrics such as taskability, Response Time (RT), knowledge capacity and utilization has also been evaluated. The average values of taskability, RT, knowledge capacity and knowledge utilisation are 0.85, 0.81, 0.55, and 0.44. The ultimate goal is to make customised teaching easier; hence, this article involves determining a student’s cognitive level by estimating their reasoning coefficient. The suggested approach analyses and categorises students’ cognitive abilities, such as memory, reasoning, problem solving, thinking, and logical reasoning, using three different reasoning coefficients. This approach assists teachers in determining the degree of intelligence of their students. 

Abirami Murugappan

Papers

A survey of keyword spotting techniques for printed document images

Cognitive computing technological trends and future research directions in healthcare - A systematic literature review

Suicidal Intention Detection in Tweets Using BERT-Based Transformers

A Hybrid Ai Framework for Multi-Label Toxicity Category Detection

Design of a Cognitive Knowledge Representation Model to Assess the Reasoning Levels of Primary School Children