Design of an Offline Handwriting Recognition System Tested on the Bangla and Korean Scripts

This dissertation presents a flexible and robust offline handwriting recognition system which is tested on the Bangla and Korean scripts. Offline handwriting recognition is one of the most challenging and yet to be solved problems in machine learning. While a few popular scripts (like Latin) have received a lot of attention, many other widely used scripts (like Bangla) have seen very little progress. Features such as connectedness and vowels structured as diacritics make it a challenging script to recognize. A simple and robust design for offline recognition is presented which not only works reliably, but also can be used for almost any alphabetic writing system. The framework has been rigorously tested for Bangla and demonstrated how it can be transformed to apply to other scripts through experiments on the Korean script whose two-dimensional arrangement of characters makes it a challenge to recognize. The base of this design is a character spotting network which detects the location of different script elements (such as characters, diacritics) from an unsegmented word image. A transcript is formed from the detected classes based on their corresponding location information. This is the first reported lexicon-free offline recognition system for Bangla and achieves a Character Recognition Accuracy (CRA) of 94.8%. This is also one of the most flexible architectures ever presented. Recognition of Korean was achieved with a 91.2% CRA. Also, a powerful technique of autonomous tagging was developed which can drastically reduce the effort of preparing a dataset for any script. The combination of the character spotting method and the autonomous tagging brings the entire offline recognition problem very close to a singular solution. Additionally, a database named the Boise State Bangla Handwriting Dataset was developed. This is one of the richest offline datasets currently available for Bangla and this has been made publicly accessible to accelerate the research progress. Many other tools were developed and experiments were conducted to more rigorously validate this framework by evaluating the method against external datasets (CMATERdb 1.1.1, Indic Word Dataset and REID2019: Early Indian Printed Documents). Offline handwriting recognition is an extremely promising technology and the outcome of this research moves the field significantly ahead

Segmentation-Free Korean Handwriting Recognition Using Neural Network Training

The idea of segmentation-free handwriting recognition has been introduced within the rise of deep learning. This technique is designed to recognize any script language/symbols as long as feedable training image set exists. The VGG-16 convolutional neural network model is used as a character spotting network using Faster R-CNN. Through the process of manual tagging, the location, size, and types of recognizable symbols are provided to train the network. This approach has been tested previously on text written in the Bangla script, where it has shown over 90% of accuracy overall. For Bangla, the network is trained and tested on Boise State Bangla Handwriting dataset. For Korean, the network is trained using the PE_92 Handwritten Korean character image database and shows promising results

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