Doctor’s Cursive Handwriting Recognition System Using Deep Learning

2019 ◽  
Author(s):  
Lovely Joy Fajardo ◽  
Nino Joshua Sorillo ◽  
Jaycel Garlit ◽  
Cia Dennise Tomines ◽  
Mideth B. Abisado ◽  
...  
Keyword(s):  
Deep Learning ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Cursive Handwriting

USING A STATISTICAL LANGUAGE MODEL TO IMPROVE THE PERFORMANCE OF AN HMM-BASED CURSIVE HANDWRITING RECOGNITION SYSTEM

2001 ◽  
Vol 15 (01) ◽  
pp. 65-90 ◽  
Author(s):  
U.-V. MARTI ◽  
H. BUNKE
Keyword(s):  
Hidden Markov ◽  
Handwriting Recognition ◽  
Language Model ◽  
Recognition System ◽  
Difficult Problem ◽  
Language Models ◽  
Linguistic Knowledge ◽  
Statistical Language Model ◽  
Cursive Handwriting ◽  
Handwritten Text

In this paper, a system for the reading of totally unconstrained handwritten text is presented. The kernel of the system is a hidden Markov model (HMM) for handwriting recognition. This HMM is enhanced by a statistical language model. Thus linguistic knowledge beyond the lexicon level is incorporated in the recognition process. Another novel feature of the system is that the HMM is applied in such a way that the difficult problem of segmenting a line of text into individual words is avoided. A number of experiments with various language models and large vocabularies have been conducted. The language models used in the system were also analytically compared based on their perplexity.

scholarly journals Artificial Neural Network for Cursive Handwriting Recognition System

2020 ◽  
Vol 9 (5) ◽  
pp. 877-880
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Neural System ◽  
Cursive Handwriting ◽  
Testing Zone ◽  
Artificial Neural ◽  
The One

Cursive Handwriting acknowledgment is an extremely testing zone because of the one of a kind styles of composing starting with one individual then onto the next. Right now, disconnected cursive composing character acknowledgment framework is portrayed utilizing an Artificial Neural Network. The highlights of every character written in the information are extricated and afterward sent to the neural system. Informational collections, having writings of various individuals are utilized in making framework. The suggested acknowledgment framework yields elevated steps of exactness when contrasted with the ordinary methodologies right now. This framework can effectively perceive cursive messages and convert them into auxiliary structure.

OFFLINE HANDWRITING RECOGNITION USING SYNTHETIC TRAINING DATA PRODUCED BY MEANS OF A GEOMETRICAL DISTORTION MODEL

2004 ◽  
Vol 18 (07) ◽  
pp. 1285-1302 ◽  
Author(s):  
T. VARGA ◽  
H. BUNKE
Keyword(s):  
Recognition Performance ◽  
Handwriting Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Training Data ◽  
Training Set ◽  
Experimental Conditions ◽  
Perturbation Model ◽  
Cursive Handwriting ◽  
Synthetic Training Data

A perturbation model for the generation of synthetic textlines from existing cursively handwritten lines of text produced by human writers is presented. The goal of synthetic textline generation is to improve the performance of an offline cursive handwriting recognition system by providing it with additional training data. It can be expected that by adding synthetic training data the variability of the training set improves, which leads to a higher recognition rate. On the other hand, synthetic training data may bias a recognizer towards unnatural handwriting styles, which could lead to a deterioration of the recognition rate. In this paper the proposed perturbation model is evaluated under several experimental conditions, and it is shown that significant improvement of the recognition performance is possible even when the original training set is large and the textlines are provided by a large number of different writers.

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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