This paper presents a comprehensive test of the principal tasks in document image analysis (DIA), starting with binarization, text line segmentation, and isolated character/glyph recognition, and continuing on to word recognition and transliteration for a new and challenging collection of palm leaf manuscripts from Southeast Asia. This research presents and is performed on a complete dataset collection of Southeast Asian palm leaf manuscripts. It contains three different scripts: Khmer script from Cambodia, and Balinese script and Sundanese script from Indonesia. The binarization task is evaluated on many methods up to the latest in some binarization competitions. The seam carving method is evaluated for the text line segmentation task, compared to a recently new text line segmentation method for palm leaf manuscripts. For the isolated character/glyph recognition task, the evaluation is reported from the handcrafted feature extraction method, the neural network with unsupervised learning feature, and the Convolutional Neural Network (CNN) based method. Finally, the Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) based method is used to analyze the word recognition and transliteration task for the palm leaf manuscripts. The results from all experiments provide the latest findings and a quantitative benchmark for palm leaf manuscripts analysis for researchers in the DIA community.

In order to preserve the Sundanese palm leaf manuscripts, some digitization campaigns have been done recently. Then, for further access in education and research, the handwritten Sundanese palm leaf manuscript dataset called Lontar Sunda dataset has been created. The dataset was constructed from 66 pages of 27 collections of Sundanese palm leaf manuscripts from the 15th century. The dataset has been carried out with manuscripts from Garut, West Java, Indonesia. This paper presents the Sundanese dataset which is publicly available for scientific use. The groundtruth includes binarized images, annotations at word level and annotations at character level. The Sundanese dataset provides useful data to test word spotting, character/symbol recognition and binarization methods, and will facilitate the evaluation of developed methods.

This paper presents the results of the Competition on the Analysis of Handwritten Text in Images of Balinese Palm Leaf Manuscripts that was organized in the context of the 15th International Conference on Frontiers in Handwriting Recognition (ICFHR-2016). This competition provides a suitable challenge for testing and evaluation of robustness for some methods, image features and descriptors which were already proposed for handwritten text analysis of document image. In this competition, three different challenges in document analysis of palm leaf manuscript images are proposed: Challenge 1: Binarization of Palm Leaf Manuscript Images, Challenge 2: Query-by-Example Word Spotting on Palm Leaf Manuscript Images, and Challenge 3: Isolated Character Recognition of Balinese Script in Palm Leaf Manuscript Images. The first handwritten Balinese palm leaf manuscript dataset, the AMADI_LontarSet, is used for performance evaluation. This paper describes the competition details including the dataset creation and the ground truth construction, the evaluation measures used, a short description of each participant as well as the performance of the all submitted methods.

This paper presents the results of the Competition on Document Image Analysis Tasks for Southeast Asian Palm Leaf Manuscripts that was organized in the context of the 16th International Conference on Frontiers in Handwriting Recognition (ICFHR-2018). For this competition, three different corpus of palm leaf manuscripts written in three different scripts and languages (Balinese, Sundanese and Khmer) are used. Four Document Image Analysis (DIA) tasks are proposed as the challenges in this competition: binarization, text line segmentation, solated character/glyph recognition, and word transliteration. The results of this competition will be very useful in benchmarking analysis for the collection of palm leaf manuscripts, accelerating, evaluating and improving the performance of existing DIA system for a new type of document collection. This paper describes the competition details including the dataset, the evaluation measures used, a short description of each participant as well as the performance of the all submitted methods.

The cognitive aspect is crucial in developing interactive and adaptive systems, including learning management systems (LMS). By understanding human cognitive processes, developers can create adaptive systems that are sustainably used by targeted users. Cognitive traits need to be explored to become part of the user model, especially especially to develop an LMS with an adaptive interface based on cognitive load. However, there is limited research exploring the correlation between cognitive load and adaptive interfaces, as well as the visualization of information, within online learning settings, including those specifically within LMS platforms. Additionally, a lack of understanding of cognitive processes renders the use of LMS static and unable to adapt to learners’ abilities. Therefore, information regarding the specific characteristics or model of cognitive load that accurately represent LMS users as triggers for adaptive interfaces needs to be further examined. A total of four experts in the cognitive field were interviewed. The interview process was conducted in a semi-structured manner to obtain information about problems in the use of e-learning, the nature of cognitive, and the process of capturing cognitive load during learning using LMS. By using a soft system methodology, the results from the interviews are mapped into a cognitive load-based LMS user model design. The initial model design includes information on working memory capacity, task performance (time, true/false rate), learning behavior (learner log), physiological (in a lab scale), and self-reporting (Likert scale). This model design is a promising first step towards a more technical process in developing adaptive and interactive systems.

Cognitive load (CL), the mental effort required to process information, plays a pivotal role in user performance and experience in various domains, particularly within computer science (CS) and information systems (IS). As technology becomes more interactive and complex, understanding and measuring CL has become increasingly important for the design of adaptive and user-centered systems. This study aims to investigate trends in CL measurement techniques within CS and IS research from 2017 to 2024, with a focus on identifying emerging tools, methods, and their applications. A systematic literature review (SLR) was conducted to provide a comprehensive overview of CL’s role in CS and IS, the methods used to detect it, and how it is analyzed across different tasks and environments. The motivation behind this research stems from the growing need to optimize user experiences and system efficiency through better CL management. Findings reveal a significant shift towards multimodal CL measurement methods, which integrate subjective, performance-based, behavioral, and physiological data. These approaches are increasingly analyzed using machine learning techniques, particularly in areas such as human-computer interaction, education, and immersive technologies like virtual reality. This research highlights the importance of accurate CL measurement and suggests future directions for enhancing adaptive system design through the integration of CL metrics.