On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net

On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple hresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples

An experimental HMM-based postal OCR system

It is almost universally accepted in speech recognition that phone- or word-level segmentation prior to recognition is neither feasible nor desirable, and in the dynamic (pen-based) handwriting recognition domain the success of segmentation-free techniques points to the same conclusion. But in image-based handwriting recognition, this conclusion is far from being firmly established, and the results presented in this paper show that systems employing character-level presegmentation can be more effective, even within the same HMM paradigm, than systems relying on sliding window feature extraction. We describe two variants of a Hidden Markov system recognizing handwritten addresses on US mail, one with presegmentation and one without, and report results on the CEDAR data set. 1. INTRODUCTION Any approach to speech and handwriting recognition must take into account that the signal is composed from a succession of alphabetic units (phonemes or graphemes). In the early work on speech recog..

Oiahcr: online isolated arabic handwritten character recognition using neural network.

In this paper, an online isolated Arabic handwritten character recognition system is introduced. The system can be adapted to achieve the demands of hand-held and digital tablet applications. To achieve this goal, despite of single neural networks, four neural networks are used, one for each cluster of characters. Feed forward back propagation neural networks are used in classification process. This approach is employed as classifiers due to the low computation overhead during training and recall process. The system recognizes on-line isolated Arabic character and achieves an accuracy rate 9٥. 7% from untrained writers and 99.1% for trained writers

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

Handwriting styles: benchmarks and evaluation metrics

Evaluating the style of handwriting generation is a challenging problem, since it is not well defined. It is a key component in order to develop in developing systems with more personalized experiences with humans. In this paper, we propose baseline benchmarks, in order to set anchors to estimate the relative quality of different handwriting style methods. This will be done using deep learning techniques, which have shown remarkable results in different machine learning tasks, learning classification, regression, and most relevant to our work, generating temporal sequences. We discuss the challenges associated with evaluating our methods, which is related to evaluation of generative models in general. We then propose evaluation metrics, which we find relevant to this problem, and we discuss how we evaluate the evaluation metrics. In this study, we use IRON-OFF dataset. To the best of our knowledge, there is no work done before in generating handwriting (either in terms of methodology or the performance metrics), our in exploring styles using this dataset.Comment: Submitted to IEEE International Workshop on Deep and Transfer Learning (DTL 2018