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The segmentation of pancreas and pancreatic tumor remain a persistent challenge for radiologists. Consequently, it is essential to develop automated segmentation methods to address this task. U-Net based models are most often used among various deep learning-based techniques in tumor segmentation. This paper introduces an innovative hybrid two-stage U-Net model for segmenting both the pancreas and pancreatic tumors. The optimization technique, used in this approach, involves a combination of meta-heuristic optimization algorithms namely, Grey Wolf Border Collie Optimization (GWBCO) technique, combining the Grey Wolf Optimization algorithm and the Border Collie Optimization algorithm. Our approach is evaluated using key parameters, such as Dice Similarity Coefficient (DSC), Jaccard Index (JI), sensitivity, specificity and precision to assess its effectiveness and achieves a DSC of 93.33 % for pancreas segmentation. Additionally, the model also achieves high DSC of 91.46 % for pancreatic tumor segmentation. This method helps in improving the diagnostic accuracy and assists medical professionals to provide treatment at an early stage with precise intervention. The method offers • Two-stage U-Net model addresses both pancreas and tumor segmentation. • Combination of two metaheuristic optimization algorithms, Grey Wolf and Border Collie for enhanced performance. • High dice similarity coefficient for pancreas and tumor segmentation.

Hand-drawn diagrams have been a standard visual communication tool in many disciplines, including architectural design, engineering, and education. The inherent diversity and absence of standardized formats of hand-drawn diagrams make it difficult to recognize them. As a result, there is an increasing need for efficient strategies and approaches for correctly identifying and interpreting hand-drawn diagrams. This research study comprehensively reviews hand-drawn diagram recognition (HDDR) techniques, emphasizing their importance and usefulness in numerous sectors. For the past ten years, articles from the Scopus database on HDDR have been extracted and reviewed. The study explores the approaches, steps, and benchmark datasets available to recognize hand-drawn diagrams. An attempt is made to get insights into the most recent state-of-the-art methodologies, their limits, and potential future advancement directions. This paper also suggests probable solutions to overcome the limitations and develop new techniques for efficiently and robustly recognizing hand-drawn diagrams.

Handwritten document recognition and classification are among the many computers related issues being studied for digitizing handwritten data. A handwritten document comprises text, diagrams, mathematical expressions, numerals, and tables. Due to the variety of writing styles and the intricacy of the written language, it has proven difficult to recognize handwritten material. As a result, numerous handwritten document recognition systems have been developed, each with unique benefits and drawbacks. The paper reviews the evolution of handwritten document recognition in qualitative and quantitative ways. Initially, the bibliometric survey is presented based on the number of articles, citations, countries, authors, etc., on handwritten document recognition in the Scopus database. Later, a survey is done on the learning techniques used for handwritten documents: text recognition, digit recognition, mathematical expression recognition, table recognition, and diagram recognition. This paper also presents the directions for future research in handwritten document recognition.

Digitization created a demand for highly efficient handwritten document recognition systems. A handwritten document consists of digits, text, symbols, diagrams, etc. Digits are an essential element of handwritten documents. Accurate recognition of handwritten digits is vital for effective communication and data analysis. Various researchers have attempted to address this issue with modern convolutional neural network (CNN) techniques. Even after training, CNN filter weights remain unchanged despite the high identification accuracy. As a result, the process cannot flexibly adapt to input changes. Hence computer vision researchers have recently become interested in Vision Transformers (ViTs) and Multilayer Perceptrons (MLPs). The shortcomings of CNNs gave rise to a hybrid model revolution that combines the best elements of the two fields. This paper analyzes how the hybrid convolutional ViT model affects the ability to recognize handwritten digits. Also, the real-time data contains noise, distortions, and varying writing styles. Hence, cleaned and uncleaned handwritten digit images are used for evaluation in this paper. The accuracy of the proposed method is compared with the state-of-the-art techniques, and the result shows that the proposed model achieves the highest recognition accuracy. Also, the probable solutions for recognizing other aspects of handwritten documents are discussed in this paper. • Analyzed the effect of convolutional vision transformer on cleaned and real-time handwritten digit images. • The model's performance improved with the implication of cross-validation and hyper-parameter tuning. • The results show that the proposed model is robust, feasible, and effective on cleaned and uncleaned handwritten digits.

Presents corrections to the paper, (Corrections to “An Overview of Hand-Drawn Diagram Recognition Methods and Applications”).