Effective Handwriting Recognition System Using Geometrical Character Analysis Algorithms

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.

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Hybrid modelling of an off line Arabic handwriting recognition system: results and evaluation

International Journal of Intelligent Enterprise ◽

10.1504/ijie.2017.087017 ◽

2017 ◽

Vol 4 (1/2) ◽

pp. 168 ◽

Cited By ~ 1

Author(s):

Ons Meddeb ◽

Mohsen Maraoui ◽

Shadi Aljawarneh

Keyword(s):

Handwriting Recognition ◽

Recognition System ◽

Hybrid Modelling

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

Sensors ◽

10.3390/s20174878 ◽

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.

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