NeuroWrite: Predictive Handwritten Digit Classification using Deep Neural Networks

The rapid evolution of deep neural networks has revolutionized the field of machine learning, enabling remarkable advancements in various domains. In this article, we introduce NeuroWrite, a unique method for predicting the categorization of handwritten digits using deep neural networks. Our model exhibits outstanding accuracy in identifying and categorising handwritten digits by utilising the strength of convolutional neural networks (CNNs) and recurrent neural networks (RNNs).In this article, we give a thorough examination of the data preparation methods, network design, and training methods used in NeuroWrite. By implementing state-of-the-art techniques, we showcase how NeuroWrite can achieve high classification accuracy and robust generalization on handwritten digit datasets, such as MNIST. Furthermore, we explore the model's potential for real-world applications, including digit recognition in digitized documents, signature verification, and automated postal code recognition. NeuroWrite is a useful tool for computer vision and pattern recognition because of its performance and adaptability.The architecture, training procedure, and evaluation metrics of NeuroWrite are covered in detail in this study, illustrating how it can improve a number of applications that call for handwritten digit classification. The outcomes show that NeuroWrite is a promising method for raising the bar for deep neural network-based handwritten digit recognition.Comment: 6 pages, 10 figure

Feature Extraction Methods for Character Recognition

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Recognition of mathematical handwriting on whiteboards

Automatic recognition of handwritten mathematics has enjoyed significant improvements in the past decades. In particular, online recognition of mathematical formulae has seen a number of important advancements. However, in reality most mathematics is still taught and developed on regular whiteboards and offline recognition remains an open and challenging task in this area. In this thesis we develop methods to recognise mathematics from static images of handwritten expressions on whiteboards, while leveraging the strength of online recognition systems by transforming offline data into online information. Our approach is based on trajectory recovery techniques, that allow us to reconstruct the actual stroke information necessary for online recognition. To this end we develop a novel recognition process especially designed to deal with whiteboards by prudently extracting information from colour images. To evaluate our methods we use an online recogniser for the recognition task, which is specifically trained for recognition of maths symbols. We present our experiments with varying quality and sources of images. In particular, we have used our approach successfully in a set of experiments using Google Glass for capturing images from whiteboards, in which we achieve highest accuracies of 88.03% and 84.54% for segmentation and recognition of mathematical symbols respectively

Improving OCR Post Processing with Machine Learning Tools

Optical Character Recognition (OCR) Post Processing involves data cleaning steps for documents that were digitized, such as a book or a newspaper article. One step in this process is the identification and correction of spelling and grammar errors generated due to the flaws in the OCR system. This work is a report on our efforts to enhance the post processing for large repositories of documents. The main contributions of this work are: • Development of tools and methodologies to build both OCR and ground truth text correspondence for training and testing of proposed techniques in our experiments. In particular, we will explain the alignment problem and tackle it with our de novo algorithm that has shown a high success rate. • Exploration of the Google Web 1T corpus to correct errors using context. We show that over half of the errors in the OCR text can be detected and corrected. • Applications of machine learning tools to generalize the past ad hoc approaches to OCR error corrections. As an example, we investigate the use of logistic regression to select the correct replacement for misspellings in the OCR text. • Use of container technology to address the state of reproducible research in OCR and Computer Science as a whole. Many of the past experiments in the field of OCR are not considered reproducible research questioning whether the original results were outliers or finessed

The Architect-Teacher’s Role in Formulating Architectural Pedagogy in China before 1952: The Examples of Huang Zuoshen and Liang Sicheng

This thesis examines China’s early modern architectural pedagogy before the 1952 restructuring of higher education under the Communist regime. In this context, it reflects on two key figures—Liang Sicheng (1901–1972) and Huang Zuoshen (1915–1975)—in their respective departments of architectural engineering at Tsinghua University (Beijing) and St. John’s University (SJU, Shanghai). I explore three themes—architect-teacher, makeshift modernity, and contested discourse—which encapsulate Huang’s and Liang’s teaching methodology and reflect their foreign-study experiences. Part 1 is dedicated to Huang: his studies at the Architectural Association (1933–1938) in Britain during its curricular revolution inspired by the Modern Architectural Research Group; his learning at the Graduate School of Design (1939–1941), not only from Gropius (the focus of previous scholarship) but also other modernists; and the SJU architecture programme Huang established in 1942, where he gathered an international faculty and promoted progressive approaches beyond Bauhaus principles. Part 2 features Liang’s environmental design pedagogy at Tsinghua: his concept of building (ying jian, culminating in his proposal for a College of Building); his methods of teaching city planning (which he added to his curriculum after the Second World War); and his influences from midwestern US institutions (i.e., the Cranbrook Academy of Art, the University of Michigan, and Taliesin) and Harvard’s Fogg Museum of Art. Finally, the thesis investigates Huang’s and Liang’s beliefs about the social position of the architect. It aligns Liang’s views on architecture’s relationship to society, engineering, and art with Huang’s commitment to architecture’s popular, scientific, and national aspects in post-1949 China. This thesis demonstrates that, despite the differences between inward-looking Beijing and outward-looking Shanghai, and between Liang’s and Huang’s respective backgrounds in the Beaux-Arts and Bauhaus modernism, these two figures embody the pedagogic plurality that characterised the establishment of architectural education in the first half of twentieth-century China