This paper evaluates the use of the conventional architecture feedforward MLP (multiple layer perceptron) and class-modular for the handwriting recognition and it also compares the results obtained with previous works in terms of recognition rate. This work presents a feature set in full detail to work with handwriting recognition. The experiments showed that the class-modular architecture is b...

Several approaches to recognize the handwritten characters and numerals such as online cursive handwriting and numerals recognition have been proposed. Most of them are based on neural network approaches. In this paper, a technique for continuous recognition of handwritten Devanagari characters is proposed. This approach employs a method based on Spatio-Temporal Artificial Neural Network (STANN...

Handwriting recognition systems rely on the existence of a corpus for training recognition models and evaluating accuracy. Creating a handwriting recognition corpus for the Bushman languages of southern Africa is difficult due to the complexities of the script used to represent them and the fact that this script cannot be represented using Unicode. To solve this problem, a semi-automatic Web-ba...

In this paper we present the first Arabic sentence dataset for on-line handwriting recognition written on tablet pc. The dataset is natural, simple and clear. Texts are sampled from daily newspapers. To collect naturally written handwriting, forms are dictated to writers. The current version of our dataset includes 154 paragraphs written by 48 writers. It contains more than 3800 words and more ...

This research project was aimed to develop an intelligent Bengali handwriting education system to improve the literacy level in Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. Here, we developed a prototype of web-based (iPhone/smartphone or computer browser) intelligent handwriting education system for autonomous learning of B...

Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten characte...

Handwriting recognition has been one of the most fascinating and challenging research areas in the field of image processing and pattern recognition in the recent year. Now a days the research work on handwriting recognition focusing toward evolving new techniques and methods that would reduce the processing time while maintaining higher recognition accuracy. A brief review of various Soft Comp...

The within-writer variability of handwriting forms one of the problems in the automatic recognition of cursive script. Variability can be handled by choosing handwriting features based upon the process of handwriting generation or upon computational models. Handwriting patterns are represented by a sequence of motor actions, i.e., "strokes", which can be identified by invariant segmentation. Ea...

Handwriting recognition is an interesting part in pattern recognition field. In the last decade, several approaches are focused on online handwriting recognition because the very rapid growth of new technologies in the field of data entry. In this paper, we propose a new system for online Arabic handwriting recognition based on beta-elliptic model which allow to segment the trajectory into segm...

Off-line handwriting recognition deals with the task of automatically recognizing handwritten text from images, for example from scanned sheets of paper. Due to the tremendous variations of writing styles encountered between different individuals, this is a very challenging task. Traditionally, a recognition system is trained by using a large corpus of handwritten text that has to be transcribe...