Online Handwriting Recognition using HMM

Basically handwriting recognition can be divided into two parts as Offline handwriting recognition and Online handwriting recognition. Highly accurate output with predefined constraints can be given by Online handwriting recognition system as it is related to size of vocabulary and writer dependency, printed writing style etc. Hidden markov model increases the success rate of online recognition system. Online handwriting recognition gives additional time information which is not present in Offline system. A Markov process is a random prediction process whose future behavior rely only on its present state, does not depend on the past state. Which means it should satisfy the Markov condition. A Hidden markov model (HMM) is a statistical markov model. In HMM model the system being modeled is assumed to be a markov process with hidden states. Hidden Markov models (HMMs) can be viewed as extensions of discrete-state Markov processes. Human-machine interaction can be drastically getting improved as On-line handwriting recognition technology contains that capability. As instead of using keyboard any person can write anything by hand with the help of digital pen or any similar equipment would be more natural. HMM build a effective mathematical models for characterizing the variance both in time and signal space presented in speech signal

Combining diverse systems for handwritten text line recognition

In this paper, we present a recognition system for on-line handwritten texts acquired from a whiteboard. The system is based on the combination of several individual classifiers of diverse nature. Recognizers based on different architectures (hidden Markov models and bidirectional long short-term memory networks) and on different sets of features (extracted from on-line and off-line data) are used in the combination. In order to increase the diversity of the underlying classifiers and fully exploit the current state-of-the-art in cursive handwriting recognition, commercial recognition systems have been included in the combined system, leading to a final word level accuracy of 86.16%. This value is significantly higher than the performance of the best individual classifier (81.26%

THE USE OF JAMBOARD IN TEACHING ENGLISH TO INCREASE JUNIOR HIGH SCHOOL STUDENTS’ MOTIVATION

Pendidikan adalah salah satu tonggak kemajuan suatu bangsa. Pendidikan masih menjadi atensi serius di tiap daerah agar mengalami peningkatan. Mutu pendidikan di SMP PGRI 6 di Surabaya sudah baik namun masih perlu terus ditingkatkan terutama dalam hal pembelajaran bahasa Inggris berbasis teknologi yang masih minim. Pembelajaran bahasa Inggris akan jauh lebih menarik jika dikemas dengan perpaduan teknologi dan disajikan kepada siswanya dalam bentuk visual learning. Sebagai solusi, dibutuhkan adanya pendampingan dalam pembelajaran bahasa Inggris secara daring berbasis teknologi dengan menerapkan aplikasi jamboard dalam pembelajaran bahasa Inggris. Jamboard adalah aplikasi berperangkat lunak yang handal yang dirancang sebagai papan tulis digital yang memudahkan perubahan secara real-time. Kegiatan Pengabdian Kepada Masyarakat (PKM) ini difokuskan pada penerapan aplikasi jamboard dalam pembelajaran bahasa Inggris pada siswa SMP PGRI 6 Surabaya. Target sasaran kegiatan PKM ini yaitu pada siswa di sekolah tersebut kelas 7, 8 dan 9 sebanyak 30 orang. Metode pengumpulan data dilakukan dengan cara observasi dan interview. Analisis data dilakukan melalui mendeskripsikan data hasil observasi dan interview. Hasil dari kegiatan pengabdian kepada masyarakat ini menunjukkan bahwa penggunaan aplikasi jamboard dapat meningkatkan motivasi dalam belajar bahasa Inggris dan penguasaan teknologi siswa SMP tersebut

An IoT System for Converting Handwritten Text to Editable Format via Gesture Recognition

Evaluation of traditional classroom has led to electronic classroom i.e. e-learning. Growth of traditional classroom doesn’t stop at e-learning or distance learning. Next step to electronic classroom is a smart classroom. Most popular features of electronic classroom is capturing video/photos of lecture content and extracting handwriting for note-taking. Numerous techniques have been implemented in order to extract handwriting from video/photo of the lecture but still the deficiency of few techniques can be resolved, and which can turn electronic classroom into smart classroom. In this thesis, we present a real-time IoT system to convert handwritten text into editable format by implementing hand gesture recognition (HGR) with Raspberry Pi and camera. Hand Gesture Recognition (HGR) is built using edge detection algorithm and HGR is used in this system to reduce computational complexity of previous systems i.e. removal of redundant images and lecture’s body from image, recollecting text from previous images to fill area from where lecture’s body has been removed. Raspberry Pi is used to retrieve, perceive HGR and to build a smart classroom based on IoT. Handwritten images are converted into editable format by using OpenCV and machine learning algorithms. In text conversion, recognition of uppercase and lowercase alphabets, numbers, special characters, mathematical symbols, equations, graphs and figures are included with recognition of word, lines, blocks, and paragraphs. With the help of Raspberry Pi and IoT, the editable format of lecture notes is given to students via desktop application which helps students to edit notes and images according to their necessity