Online Handwriting Recognition by the Symbolic Histograms Approach (original) (raw)

On-line handwriting recognition system based on graph matching and genetic algorithm

In automatic recognition of unrestricted handwriting the ambiguities can be solved by top-down processing. However, automatic systems never have access to the extended background knowledge available to human readers. In order to replace this higher-level information we need to improve the reliability of the bottom-up processing. A handwriting-recognition system can be split up into six discrete blocks: (1) digitizing, word segmentation, pre-processing, and segmentation into strokes, (2) normalization of global handwriting parameters, (3) extraction of features per stroke, (4) allograph recognition, (5) optional word hypothesization, and, in order to allow recognition (6) a learning phase. The present paper discusses the design of three of these processing blocks: normalization, allograph recognition, and learning and brie y speci es feature extraction. Normalization concerns orientation, size, and slant. However, various alternative algorithms can be chosen and some algorithms yield more reliable results than others. A mechanism is proposed that will, sooner or later, nd the most appropriate normalization algorithms. Consequently, the features extracted from each stroke in the handwriting pattern will be more uniform within a writer and even between writers. In the recognition phase, handwriting patterns are segmented into allographs using an algorithm that can handle allographs with various numbers of strokes and with optional connection strokes between them. In order to teach the recognizer the allographs a method has been designed that builts non-interactively a lexicon of allographs by automatically discovering the allographs in a large corpus of cursive script.

Handwriting Recognition Systems: An Overview

Committing words to paper in handwriting is a uniquely human act, performed daily by millions of people. If you were to present the idea of "decoding" handwriting to most people, perhaps the first idea to spring to mind would be graphology, which is the analysis of handwriting to determine its authenticity (or perhaps also the more non-scientific determination of some psychological character traits of the writer). But the more mundane, and more frequently overlooked, "decoding" of handwriting is handwriting recognition-the process of figuring out what words and letters the scribbles and scrawls on the paper represent.

A Set of Handwriting Features for Use in Automated Writer Identification()

Journal of forensic sciences, 2017

A writer's biometric identity can be characterized through the distribution of physical feature measurements ("writer's profile"); a graph-based system that facilitates the quantification of these features is described. To accomplish this quantification, handwriting is segmented into basic graphical forms ("graphemes"), which are "skeletonized" to yield the graphical topology of the handwritten segment. The graph-based matching algorithm compares the graphemes first by their graphical topology and then by their geometric features. Graphs derived from known writers can be compared against graphs extracted from unknown writings. The process is computationally intensive and relies heavily upon statistical pattern recognition algorithms. This article focuses on the quantification of these physical features and the construction of the associated pattern recognition methods for using the features to discriminate among writers. The graph-based system d...

A Handwriting Recognition System Based on Properties of the Human Motor System

1990

The human reader of handwriting is unaware of the amount of back-ground knowledge that is constantly being used by a massive parallel computer, his brain, to decipher cursive script. Artificial cursive script recognizers do not have access to a comparable source of knowledge or of comparable computational power to perform top-down processing. Therefore, in an artificial script recognizer, there is a strong demand for reliable bottom-up processing. For the recognition of unrestricted script consisting of arbitrary character sequences, on-line recorded handwriting signals offer a more solid basis than the optically obtained grey-scale image of a written pen trace, because of the temporal information and the inherent vectorial description of shape. The enhanced bottom-up processing is based on implementing knowledge of the motor system in the handwriting recognition system. The bottom-up information will already be sufficient to recognize clearly written and unambiguous input. However, ambiguous shape sequences, such as mmm vs n..n..n.. or ddd vs clclcl, and sloppy stroke patterns still require top-down processing. The present paper discusses the handwriting recognition system as being developed at the NICI. The system contains six major modules: (1) On-line digitizing, pre-processing of the movements and segmentation into strokes. (2) Normalization of global handwriting parameters. (3) Extraction of motorically invariant, real-valued, feature values per stroke to form a multi-dimensional feature vector and subsequent feature vector quantization by a self-organizing two-dimensional Kohonen network. (4) Allograph construction, using a second network of transition probabilities between cell activation patterns of the Kohonen network. (5) Optional word hypothesization. (6) The system has to be trained by supervised learning, the user indicating prototypical stroke sequences and their symbolic interpretation (letter or N-gram naming).

Handwriting Recognition by Machine Learning

International Journal of Innovative Technology and Exploring Engineering, 2019

Handwriting is one of the most natural ways of communication among people. The handwriting recognition task is the main concern of scientific community because handwriting can be varies with the same person or from one person to another hence the prediction of human behavior through handwriting is a complex task. Earlier the handwriting analysis has been done by graphologists but due to the modernization and the arrival of digital world the handwriting analysis can be done with the help of computer aided machines. Different software and algorithms has been defined to do the analysis. In the new world of machine learning handwriting recognition and the prediction of human behavior can be done by using different techniques of machine learning which increase the speed of analysis This paper studies the recent advances and the trends in the field of handwriting recognition by machine learning

Optical character recognition for cursive handwriting

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002

ÐIn this paper, a new analytic scheme, which uses a sequence of segmentation and recognition algorithms, is proposed for offline cursive handwriting recognition problem. First, some global parameters, such as slant angle, baselines, and stroke width and height are estimated. Second, a segmentation method finds character segmentation paths by combining gray scale and binary information. Third, Hidden Markov Model (HMM) is employed for shape recognition to label and rank the character candidates. For this purpose, a string of codes is extracted from each segment to represent the character candidates. The estimation of feature space parameters is embedded in HMM training stage together with the estimation of the HMM model parameters. Finally, the lexicon information and HMM ranks are combined in a graph optimization problem for word-level recognition. This method corrects most of the errors produced by segmentation and HMM ranking stages by maximizing an information measure in an efficient graph search algorithm. The experiments in dicate higher recognition rates compared to the available methods reported in the literature.

A Review on Recognition of Online Handwriting in Different Scripts

Online handwriting recognition or character recognition is the process in which a handwritten message is recognized by processing the handwritten data. It is the process of converting handwritten characters to machine format. In handwriting, the strokes are composed of two coordinate trace in between pen down and pen up labels. Wide range of features is extracted to perform the recognition. Many research works have been done for English, Japanese, Chinese and Korean languages. During the past decade a vast amount of research has also been done on some Indian scripts, viz., Malayalam, Telugu, Devanagari, Gurumukhi, Hindi, Bangla, Assamese, etc. The work presented in this paper deals with the various processes taken up by the researchers in recognizing of the handwriting of various scripts. In this paper, a detailed study of various methods and classifiers used by the researchers to recognize the scripts are made.

A Research on Handwritten Text Recognition

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

Over the years we have started accumulating handwritten documents, like pdfs, doc files and numerous other formats for reading, writing and studying. Often we come across situations where we need to utilize the text of those documents. Manually transcribing large amounts of handwritten data is an arduous process that’s bound to be fraught with errors. Automated handwriting recognition can drastically cut down on the time required to transcribe large volumes of text, and also serve as a framework for developing future applications of machine learning. Recognition can be offline or online and both can be implemented in applications to progressively learn based on the user’s feedback while performing offline learning on data in parallel. Some recognition systems identify strokes, others apply recognition on a single character or entire words. Some steps involved in the area(in no particular order): Image preprocessing, Segmentation, Classification & Recognition, Feature Extraction. Fin...