The task of Handwritten Text Recognition (HTR) still persists in computer vision and machine learning because there are numerous intra-class and inter-class differences in handwritten characters. In other words, differences in character shapes, stroke widths, styles, slants, spacings, and pen pressures result in great challenges related to automatic recognition. The inability to use HTR technology hinders the access to the information in different forms, e.g., in medical records, legal documents, educational tests, historic archive materials, etc. It is obvious that overcoming this challenge will facilitate more opportunities regarding information availability and automated processing of documents. The goal of this paper is to develop an end-to-end Handwritten Script to Digital Text Predictor (HSDTP) that will be able to convert images of handwritten characters to machine-readable text. In order to implement this goal, the CNN model trained on the MNIST database was developed and optimized for CPU-based hardware implementation without any need in special GPU. Thus, six successive steps should be implemented: image capture, pre-processing, character segmentation, character classification, reconstruction, and deployment of HSDTP as web service. Pre-processing stage involves such operations as grayscale transformation, noise filtering with Gaussian blur, binarization based on the Otsu method and normalization with resizing to 28 × 28 pixels. Character segmentation is done by contour detection; characters are recognized by CNN with prediction scores.
S. Vijayakumar, M. Arun, M.Prakash et al.· 2026 International Conferenc...· 0 citations
Workplaces in construction and industry suffer from a significant number of workplace accidents because of a lack of safety mechanisms like helmets and high-visibility vests. In order to solve this issue, an automatic detection system for detecting the presence of the worker's helmet & safety vest using a deep learning model is developed. You Only Look Once – Neural Architecture Search (YOLO-NAS) algorithm was chosen to be used in the model because of its fast and high-quality detection process. A dataset with images containing healthy workers wearing helmets & safety vests (with some workers appearing in different safety vests) is collected and used as the training set. The resulting model is then saved for future usage in making predictions with new images. Then, this model is connected to Streamlit, which provides a convenient Web-based user interface through which a user can provide an image as an input to the model. The trained neural network is applied to the input image, and all detected objects that correspond to helmet & safety vest are put in bounding boxes on the input image. Moreover, each of the bounding boxes is labeled with the name of the object and confidence score of the detection. Thus, a clear visualization of the safety equipment worn by the worker is provided. Overall, the developed detection system significantly decreases the human intervention needed for visual verification of the safety helmet or vest. The described system is a user-friendly and cost-effective way of evaluating workplace safety compliance.
S. Vijayakumar, Loganathan Nachimuthu, Balasubramaniam C et al.· 2026 International Conferenc...· 0 citations