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

Approaches Used to Recognise and Decipher Ancient Inscriptions: A Review

Inscriptions play a vital role in historical studies. In order to boost tourism and academic necessities, archaeological experts, epigraphers and researchers recognised and deciphered a great number of inscriptions using numerous approaches. Due to the technological revolution and inefficiencies of manual methods, humans tend to use automated systems. Hence, computational archaeology plays an important role in the current era. Even though different types of research are conducted in this domain, it still poses a big challenge and needs more accurate and efficient methods. This paper presents a review of manual and computational approaches used to recognise and decipher ancient inscriptions.Keywords: ancient inscriptions, computational archaeology, decipher, script

Design of CNN architecture for Hindi Characters

Handwritten character recognition is a challenging problem which received attention because of its potential benefits in real-life applications. It automates manual paper work, thus saving both time and money, but due to low recognition accuracy it is not yet practically possible. This work achieves higher recognition rates for handwritten isolated characters using Deep learning based Convolutional neural network (CNN). The architecture of these networks is complex and plays important role in success of character recognizer, thus this work experiments on different CNN architectures, investigates different optimization algorithms and trainable parameters. The experiments are conducted on two different types of grayscale datasets to make this work more generic and robust. One of the CNN architecture in combination with adadelta optimization achieved a recognition rate of 97.95%. The experimental results demonstrate that CNN based end-to-end learning achieves recognition rates much better than the traditional techniques

Recognition Character Sanskrit Using Convolution Neural Network

This research presents a pioneering approach using Convolutional Neural Networks (CNNs) for character recognition in Sanskrit, a language renowned for its intricate script and diverse character set. Addressing challenges posed by Sanskrit's complex script and historical variations in writing styles, we developed a CNN-based model that undergoes meticulous preprocessing to enhance image quality and normalize writing styles. Trained on a substantial dataset of annotated Sanskrit characters, our model showcases remarkable accuracy in recognizing Sanskrit characters, even amidst noise and diverse writing styles. This achievement holds significant implications for digitizing ancient manuscripts, aiding linguistic research, and preserving cultural heritage. Automating Sanskrit character recognition accelerates the analysis of Sanskrit texts, offering insights into linguistic evolution, cultural practices, and historical narratives. Moreover, this research lays a foundation for advancing character recognition techniques in complex scripts and languages, fostering opportunities for preserving and exploring diverse cultural heritages worldwide

Neural Networks for Fingerprint Recognition

After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to 20 individuals. The error rate currently achieved is less than 0.5%. Additional results, extensions, and possible applications are also briefly discussed

기기 상에서의 심층 신경망 개인화 방법

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. Egger, Bernhard.There exist several deep neural network (DNN) architectures suitable for embedded inference, however little work has focused on training neural networks on-device. User customization of DNNs is desirable due to the difficulty of collecting a training set representative of real world scenarios. Additionally, inter-user variation means that a general model has a limitation on its achievable accuracy. In this thesis, a DNN architecture that allows for low power on-device user customization is proposed. This approach is applied to handwritten character recognition of both the Latin and the Korean alphabets. Experiments show a 3.5-fold reduction of the prediction error after user customization for both alphabets compared to a DNN trained with general data. This architecture is additionally evaluated using a number of embedded processors demonstrating its practical application.내장형 기기에서 심층 신경망을 추론할 수 있는 아키텍처들은 존재하지만 내장형 기기에서 신경망을 학습하는 연구는 별로 이뤄지지 않았다. 실제 환경을 반영하는 학습용 데이터 집합을 모으는 것이 어렵고 사용자간의 다양성으로 인해 일반적으로 학습된 모델이 충분한 정확도를 가지기엔 한계가 존재하기 때문에 사용자 맞춤형 심층 신경망이 필요하다. 이 논문에서는 기기상에서 저전력으로 사용자 맞춤화가 가능한 심층 신경망 아키텍처를 제안한다. 이러한 접근 방법은 라틴어와 한글의 필기체 글자 인식에 적용된다. 라틴어와 한글에 사용자 맞춤화를 적용하여 일반적인 데이터로 학습한 심층 신경망보다 3.5배나 작은 예측 오류의 결과를 얻었다. 또한 이 아키텍처의 실용성을 보여주기 위하여 다양한 내장형 프로세서에서 실험을 진행하였다.Abstract i Contents iii List of Figures vii List of Tables ix Chapter 1 Introduction 1 Chapter 2 Motivation 4 Chapter 3 Background 6 3.1 Deep Neural Networks 6 3.1.1 Inference 6 3.1.2 Training 7 3.2 Convolutional Neural Networks 8 3.3 On-Device Acceleration 9 3.3.1 Hardware Accelerators 9 3.3.2 Software Optimization 10 Chapter 4 Methodology 12 4.1 Initialization 13 4.2 On-Device Training 14 Chapter 5 Implementation 16 5.1 Pre-processing 16 5.2 Latin Handwritten Character Recognition 17 5.2.1 Dataset and BIE Selection 17 5.2.2 AE Design 17 5.3 Korean Handwritten Character Recognition 21 5.3.1 Dataset and BIE Selection 21 5.3.2 AE Design 21 Chapter 6 On-Device Acceleration 26 6.1 Architecure Optimizations 27 6.2 Compiler Optimizations 29 Chapter 7 Experimental Setup 30 Chapter 8 Evaluation 33 8.1 Latin Handwritten Character Recognition 33 8.2 Korean Handwritten Character Recognition 38 8.3 On-Device Acceleration 40 Chapter 9 Related Work 44 Chapter 10 Conclusion 47 Bibliography 47 요약 55 Acknowledgements 56Maste

Influence of graphical weights’ interpretation and filtration algorithms on generalization ability of neural networks applied to digit recognition

In this paper, the method of the graphical interpretation of the single-layer network weights is introduced. It is shown that the network parameters can be converted to the image and their particular elements are the pixels. For this purpose, weight-to-pixel conversion formula is used. Moreover, new weights’ modification method is proposed. The weight coefficients are computed on the basis of pixel values for which image filtration algorithms are implemented. The approach is applied to the weights of three types of the models: single-layer network, two-layer backpropagation network and the hybrid network. The performance of the models is then compared on two independent data sets. By means of the experiments, it is presented that the adjustment of the weights to new values decreases test error value compared to the error obtained for initial set of weights