Handwritten Text Recognition (HTR) is essential for digitizing historical documents in different kinds of archives. In this study, we introduce a hybrid form archive written in French: the Belfort civil registers of births. The digitization of these historical documents is challenging due to their unique characteristics such as writing style variations, overlapped characters and words, and marginal annotations. The objective of this survey paper is to summarize research on handwritten text documents and provide research directions toward effectively transcribing this French dataset. To achieve this goal, we presented a brief survey of several modern and historical HTR offline systems of different international languages, and the top state-of-the-art contributions reported of the French language specifically. The survey classifies the HTR systems based on techniques employed, datasets used, publication years, and the level of recognition. Furthermore, an analysis of the systems’ accuracies is presented, highlighting the best-performing approach. We have also showcased the performance of some HTR commercial systems. In addition, this paper presents a summarization of the HTR datasets that publicly available, especially those identified as benchmark datasets in the International Conference on Document Analysis and Recognition (ICDAR) and the International Conference on Frontiers in Handwriting Recognition (ICFHR) competitions. This paper, therefore, presents updated state-of-the-art research in HTR and highlights new directions in the research field.

An intelligent strabismus detection method based on convolution neural network

Smart devices have become one of the most important facilities in managing the data in the organizations recently. In this research, Android Application has been developed with taking advantage of Quick Response codes technology for storing different types of data (ex: documents, goods or employees) within organizations, and retrieving it on demand on the smart device screen or sharing it to other devices as Excel reports. Discussion of the study assumptions, delimitations, and limitations resulted in developing several approaches for tackling the process of generating unique QR codes representing the items in the organizations, in addition to developing a QR code reader by making use of the camera in the smart device. Office goods have been chosen in this research project as a data sample. Based on the experiments, the findings have proven that QR code technology is highly useful in the data management process, for storing and retrieving information within smart devices, showing a high response speed and accuracy.

Background: The proliferation of digital devices, such as smartphones, TVs, tablets, and laptops, has raised concerns about the potential long-term impact on eye health, particularly from blue light emitted by screens. Related studies suggested that prolonged exposure to blue light may contribute to visual impairments or discomfort. Objective: This research introduces an innovative machine learning approach aimed at diagnosing such visual impairments by automatically detecting the iris center in images using a combination of the Haar Cascade Classifier and Circular Hough Transform algorithm. Methods: The proposed methodology has three phases. The first is the development of a diverse facial image dataset from various datasets through three steps: designing an AdaBoost-based cascaded classifier that identifies the face and the region around the eyes; subsequently, iris boundaries are labeled, where an optimized Hough Transform approach determines its center. Further, the model segments the eye into three regions to ascertain the iris position cause for the deviation of eyes. Finally, RBFN is used to classify five classes of eye strabismus with high accuracy on a horizontal axis. Results: The proposed system presents very promising results with an accuracy equal to 97.5%, precision 0.97834, specificity 0.33333, and recall 0.99632 in iris center coordinates extraction of five classes of horizontal strabismus classification. These findings were validated over two different datasets in order to prove the strength of this model in detecting eye disorders under different light and photo scenarios. Conclusions: This method may be acting like a bridge between computer vision and ophthalmology in ocular health assessment and treatment. Further improvements may open further horizons regarding automated eye detection and diagnosis.