Perbandingan Akurasi Pengenalan Karakter Plat Nomor Menggunakan Tesseract Dan Data Latih Emnist

Plat nomor merupakan identitas wajib terdiri dari huruf dan angka yang ada pada kendaraan. Plat nomor dapat dimanfaatkan dalam berbagai kebutuhan seperti sistem parkir, pengawasan lalu lintas, dan pengecekan identitas ketika terjadi kecelakaan. Pengenalan karakter dapat menggunakan Optical Character Recognition (OCR) yang melakukan metode template matching pada huruf dan angka. Menggunakan Convolutional Neural Network dengan melatih data EMINST untuk melakukan pengenalan karakter. Tujuan penelitian ini sebagai perbandingan penggunaan metode OCR menggunakan Tesseract dan CNN dalam melakukan pengenalan karakter. Data yang diuji sebanyak 58 citra mobil dengan 36 kelas karakter yang terdiri dari huruf dan angka. Pengujian pengenalan karakter menggunakan CNN pada data latih EMNIST menghasilkan kinerja yang kurang baik dengan 11 citra miliki akurasi diatas 75%. Penelitian ini menghasilkan pengenalan karakter terbaik pada Tesseract-OCR menggunakan segmentasi karakter pada plat nomor dengan 44 citra memiliki akurasi diatas 75%

Interface Development for Digitization of Documents Using OCR

The purpose of this thesis is to develop a semi-automated interface that uses Optical Character Recognition (OCR) routines to identify text-based information from a large volume of digitized drawings associated with the oil and gas industry. The identified information is presented in an appropriate interface for any necessary manual modification, with the target of improving the efficiency of maintaining large amounts of older documents. The thesis outlines the design of the interface and the implementation of Tesseract OCR engine, in combination with tailor-made functions and classes that leverage OpenCV to enhance the recognition processThe purpose of this thesis is to develop a semi-automated interface that uses Optical Character Recognition (OCR) routines to identify text-based information from a large volume of digitized drawings associated with the oil and gas industry. The identified information is presented in an appropriate interface for any necessary manual modification, with the target of improving the efficiency of maintaining large amounts of older documents. The thesis outlines the design of the interface and the implementation of Tesseract OCR engine, in combination with tailor-made functions and classes that leverage OpenCV to enhance the recognition proces

Interface Development for Digitization of Documents Using OCR

The purpose of this thesis is to develop a semi-automated interface that uses Optical Character Recognition (OCR) routines to identify text-based information from a large volume of digitized drawings associated with the oil and gas industry. The identified information is presented in an appropriate interface for any necessary manual modifica- tion, with the target of improving the efficiency of maintaining large amounts of older documents. The thesis outlines the design of the interface and the implementation of Tesseract OCR engine, in combination with tailor-made functions and classes that lever- age OpenCV to enhance the recognition process.The purpose of this thesis is to develop a semi-automated interface that uses Optical Character Recognition (OCR) routines to identify text-based information from a large volume of digitized drawings associated with the oil and gas industry. The identified information is presented in an appropriate interface for any necessary manual modifica- tion, with the target of improving the efficiency of maintaining large amounts of older documents. The thesis outlines the design of the interface and the implementation of Tesseract OCR engine, in combination with tailor-made functions and classes that lever- age OpenCV to enhance the recognition process

일반적인 문자 이미지의 언어분류

학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 협동과정 계산과학전공, 2021. 2. 강명주.As other machine learning fields, there has been a lot of progress in text detection and recognition to obtain text information contained in images since the deep learning era. When multiple languages are mixed in the im- age, the process of recognition typically goes through a detection, language classification and recognition. This dissertation aims to classify languages of image patches which are the results of text detection. As far as we know, there are no prior research exactly targeting language classification of images. So we started from basic backbone networks that are used commonly in many other general object detection fields. With a ResNeSt-based network which is based on Resnet and automated pre-processing of ground-truth data to improve classification performance, we can achieve state of the art record of this task with a public benchmark dataset.다른 기계학습분야와 마찬가지로, 이미지가 담고 있는 문자정보를 얻어 내려는 문자인식 분야에서도 딥러닝 이후 많은 진전이 있었다. 인식의 과정은 통상적으로 문자검출, 문자인식의 과정을 차례로 거치는데, 다수의 언어가 혼재할 경우 검출과 인식 사이에 언어분류 단계를 한번 더 거치는 것이 보통 이다. 본연구는문자검출이후의단계에서이미지패치들을각언어에따라 분류하는 것을 목표로 한다. 분류작업만을 전문적으로 다룬 선행연구가 없으 므로, 일반적인 객체검출에서 쓰이는 네트워크 중에서 적절한 것을 선택하고 응용하였다. ResNeSt를 기반으로한 네트워크와 자동화된 전처리 과정을 통해 공개된 벤치마크 데이터셋을 기준으로 가장 좋은 기록을 달성할 수 있었다.Abstract i 1 Introduction 1 1.1 OpticalCharacterRecognition.................. 1 1.2 DeepLearning........................... 2 2 Backgrounds 4 2.1 Detection ............................. 4 2.2 Recognition ............................ 5 2.3 LanguageClassification...................... 6 2.4 Multi-lingualText(MLT)..................... 7 2.5 ConvolutionalNeuralNetwork(CNN) . . . . . . . . . . . . . . 7 2.6 AttentionMechanism....................... 8 2.7 RelatedWorks........................... 9 2.7.1 Detectors ......................... 9 2.7.2 Recognizers ........................ 14 2.7.3 End-to-end methods (detector + recognizer) . . . . . . 14 2.8 Dataset .............................. 15 2.8.1 ICDARMLT ....................... 15 2.8.2 Syntheticdata:Gupta.................. 17 2.8.3 COCO-Text........................ 17 3 Proposed Methods 18 3.1 BaseNetworkSelection...................... 18 3.1.1 Googlenet ......................... 18 3.1.2 ShufflenetV2 ....................... 20 3.1.3 Resnet........................... 21 3.1.4 WideResnet........................ 23 3.1.5 ResNeXt.......................... 24 3.1.6 ResNeSt(Split-Attention network) ............ 24 3.1.7 Densenet.......................... 25 3.1.8 EfficientNet ........................ 25 3.1.9 Automaticsearch:AutoSTR .............. 27 3.2 Methods.............................. 28 3.2.1 Groundtruthcleansing.................. 28 3.2.2 Divide-and-stack ..................... 32 3.2.3 Usingadditionaldata................... 33 3.2.4 OHEM........................... 34 3.2.5 Network using the number of characters . . . . . . . . 35 3.2.6 UseofR-CNNstructure ................. 36 3.2.7 Highresolutioninput................... 39 3.2.8 Handling outliers using variant of OHEM . . . . . . . . 39 3.2.9 Variable sized input images using the attention . . . . 41 3.2.10 Classbalancing ...................... 41 3.2.11 Finetuningonspecificclasses.............. 42 3.2.12 Optimizerselection.................... 42 3.3 Result ............................... 42 4 Conclusion 44 Abstract (in Korean) 49Docto

Development of Automatic Digitization of Truck Number in Open Cast Mines Using Microcontroller

Geological condition in mines appears to be extremely complicated and there are many intelligence security problems. Production is falsely transfer by the unauthorized truck from mine pits also at loading point. It also lifted in wrong ways by malfunctioning of the truck weight in Weigh Bridge. Mining organizations are under the control of mafia and countless can be added to the mines mafia. An intelligence security system is need to monitor truck number in automatically using image acquisition method, automatic detection, recognition process, communication technology, information technology and microcontroller innovation to understand the working specification of the mining region. Tracking of the number plate from the truck is an important task, which demands intelligent solution. Intelligent surveillance in open casts mine security network using data accession is a prime task that protects the secure production of mines. So automatic truck number recognition technique is used to recognize the registration number of the truck which is used for transferring the mine production as well as track record the amount of the production. It also preserves the mines and thus improving its security. For extraction and recognition of number plate from truck image the system is uses MATLAB software tool. It is assumed that images of the truck have been captured from digital camera. The data acquisition terminal uses the PIC16F877A microcontroller as a core chip for sending data. The data are communicated through USB to TTL converter (RS232) with the main circuit to realize intelligent monitoring. To store the data in permanently it is uses EEPROM chip. Alphanumeric Characters on plate has been extracted and recognized using template images of alphanumeric characters. The proposed system performs the real time data monitoring to recognize the registration number plate of the trucks for getting required important information. It also provides to maintenance the history of data and support access contro