Progress in the Raytheon BBN Arabic Offline Handwriting Recognition System

2014 ◽  
Author(s):  
Huaigu Cao ◽  
Prem Natarajan ◽  
Xujun Peng ◽  
Krishna Subramanian ◽  
David Belanger ◽  
...  
Keyword(s):  
Handwriting Recognition ◽  
Recognition System ◽  
Offline Handwriting Recognition

A FULLY AUTOMATED OFFLINE HANDWRITING RECOGNITION SYSTEM INCORPORATING RULE BASED NEURAL NETWORK VALIDATED SEGMENTATION AND HYBRID NEURAL NETWORK CLASSIFIER

2004 ◽  
Vol 18 (07) ◽  
pp. 1267-1283 ◽  
Author(s):  
MOUMITA GHOSH ◽  
RANADHIR GHOSH ◽  
BRIJESH VERMA
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Solution Method ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Neural Network Classifier ◽  
Hybrid Neural Network ◽  
Rule Based ◽  
Extraction Algorithm ◽  
Offline Handwriting Recognition

In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing.

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

2020 ◽  
Author(s):  
Nishatul Majid
Keyword(s):  
Character Recognition ◽  
Singular Solution ◽  
Recognition Accuracy ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Research Progress ◽  
Writing System ◽  
Flexible Architectures ◽  
Offline Handwriting Recognition ◽  
Alphabetic Writing

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.

Machine Learning Techniques in Handwriting Recognition

2012 ◽  
pp. 12-29 ◽  
Author(s):  
Hong Lee ◽  
Brijesh Verma ◽  
Michael Li ◽  
Ashfaqur Rahman
Keyword(s):  
Machine Learning ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Research Area ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Handwritten Text ◽  
Learning Techniques ◽  
Active Research ◽  
Offline Handwriting Recognition

Handwriting recognition is a process of recognizing handwritten text on a paper in the case of offline handwriting recognition and on a tablet in the case of online handwriting recognition and converting it into an editable text. In this chapter, the authors focus on offline handwriting recognition, which means that recognition system accepts a scanned handwritten page as an input and outputs an editable recognized text. Handwriting recognition has been an active research area for more than four decades, but some of the major problems still remained unsolved. Many techniques, including the machine learning techniques, have been used to improve the accuracy. This chapter focuses on describing the problems of handwriting recognition and presents the solutions using machine learning techniques for solving major problems in handwriting recognition. The chapter also reviews and presents the state of the art techniques with results and future research for improving handwriting recognition.

scholarly journals ClothFace: A Batteryless RFID-Based Textile Platform for Handwriting Recognition

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4878
Author(s):  
Han He ◽  
Xiaochen Chen ◽  
Adnan Mehmood ◽  
Leevi Raivio ◽  
Heikki Huttunen ◽  
...  
Keyword(s):  
Integrated Circuits ◽  
Radio Frequency Identification ◽  
Handwriting Recognition ◽  
Cost Effective ◽  
Recognition System ◽  
Rfid Reader ◽  
Textile Antenna ◽  
Recognition Ability ◽  
Frequency Identification ◽  
Sequential Information

This paper introduces a prototype of ClothFace technology, a battery-free textile-based handwriting recognition platform that includes an e-textile antenna and a 10 × 10 array of radio frequency identification (RFID) integrated circuits (ICs), each with a unique ID. Touching the textile platform surface creates an electrical connection from specific ICs to the antenna, which enables the connected ICs to be read with an external UHF (ultra-haigh frequency) RFID reader. In this paper, the platform is demonstrated to recognize handwritten numbers 0–9. The raw data collected by the platform are a sequence of IDs from the touched ICs. The system converts the data into bitmaps and their details are increased by interpolating between neighboring samples using the sequential information of IDs. These images of digits written on the platform can be classified, with enough accuracy for practical use, by deep learning. The recognition system was trained and tested with samples from six volunteers using the platform. The real-time number recognition ability of the ClothFace technology is demonstrated to work successfully with a very low error rate. The overall recognition accuracy of the platform is 94.6% and the accuracy for each digit is between 91.1% and 98.3%. As the solution is fully passive and gets all the needed energy from the external RFID reader, it enables a maintenance-free and cost-effective user interface that can be integrated into clothing and into textiles around us.

Sign in / Sign up

Export Citation Format

Share Document