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An Efficient Method For Online Cursive Handwriting Strokes Reordering.
Abstract and Figures
In the framework of online cursive handwriting recognition, we present an efficient method for reordering the sequence of strokes composing handwriting in two special cases of interest: the horizontal bar of the character "t" and the dot of the character "i". The proposed method exploits shape information for selecting the strokes that most likely correspond to the features of interest, and layout and topological information for locating the strokes representing the body of the characters to which the features belong to. The method does not depend on the specific algorithm used for detecting the elementary strokes in which the electronic ink may be decomposed into. The performance of our method, evaluated on a data set of cursive words produced by 50 different writers, has shown a correct reordering of the sequence in more than 85% of the cases. Thus, the proposed method allows obtaining a more stable and invariant description of the electronic ink in terms of elementary stroke sequences, and therefore can be helpfully used as a preprocessing step for both segmentation-based and word-based handwriting recognition systems.
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... iv Indic and Arabic scripts have delayed strokes (strokes written after main stroke like dots, bars) with main strokes which make the CR task complex for online text mode. It could be handled during pre-processing phase like re-ordering the sequence of strokes, giving reference number to delayed strokes and detecting the ambiguous zones, etc. (De Stefano and Marcelli 2004;Su et al. 2009;Ghods et al. 2013). ...
The cultural and regional diversity across the world and specifically in India has given birth to a large number of writing systems and scripts having a variety of character sets. For scripts having a larger character set, just a simple keyboard with limited character set is not the optimal way for providing inputs to the computer. Variations in individual handwriting due to mood swings, changes in medium of writing, changes in writing styles, etc. pose a challenge before the character recognition (CR) research community. Similar kinds of symbols in various scripts and languages act as a big barrier in multilingual CR. Lack of benchmark results and corpora for multilingual CR hinder the research in multilingual CR. There have been only a limited number of articles for optimal combination of features and classifiers to process multilingual data. Multilingual CR has least explored the Indic scripts. This paper presents a detailed review and analysis of the work done in multilingual online as well as offline CR for Indic and non-Indic scripts. The paper mainly contributes in two ways: Firstly, it provides a clear perspective about various phases of monolingual and multilingual CR; and secondly, identifies the major deficiencies in monolingual and multilingual CR for printed and handwritten text. It contributes by giving an in-depth view of work done at each phase including data acquisition, pre-processing, segmentation, feature extraction, recognition and post-processing of CR. Issues to be resolved at each phase have also been elaborated. The recent work done using Deep and Shallow architectures has been analysed. Tools used for these architectures have been compared to highlight their pros and cons. The present work also suggests how further research can be conducted in the field of monolingual and multilingual CR. The problems such as CR in hybrid documents, identifying more reliable features, resolving issues of similar characters, identifying optimal combination strategies for deep and shallow architectures, etc. need to be tackled in future research.
Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered
This survey describes the state of the art of online handwriting
recognition during a period of renewed activity in the field. It is
based on an extensive review of the literature, including journal
articles, conference proceedings, and patents. Online versus offline
recognition, digitizer technology, and handwriting properties and
recognition problems are discussed. Shape recognition algorithms,
preprocessing and postprocessing techniques, experimental systems, and
commercial products are examined
An implementation of a model for recognition of handwriting with
variability that is combinational and syntactic is presented. The work
is a synthesis of theoretical aspects and observations of handprinting
styles in a large collection gathered during the development of a
commercial online character recognition system. Several physical factors
affecting variability in handprint are reviewed. A combinatorial
predictive model for these factors and its use in a particular design of
a device for online character recognition is presented, in which the
variability effects are treated separately from the recognition process.
Beyond the use in this system, the model implies several substantial
testing and performance problems for reliable handwritten character
recognition
For large-alphabet languages, like Japanese, handwriting input
using an online recognition technique is essential for input accuracy
and speed. However, there are serious problems that prevent high
recognition accuracy of unconstrained handwriting. First, the thousands
of ideographic Japanese characters of Chinese origin (called Kanji) can
be written with wide variations in the number and order of strokes and
significant shape distortions. Also, writing box-free recognition of
characters is required to create a better man-machine interface. Intense
research performed over the past 15 years to answer the most pressing
recognition problems is described. Prototype systems are also described.
The man-machine interfaces made possible by online handwriting
recognition and anticipated advances in both hardware and software are
discussed
Cursive script word recognition is the problem of transforming a word from the iconic form of cursive writing to its symbolic form. Several component processes of a recognition system for isolated offline cursive script words are described. A word image is transformed through a hierarchy of representation levels: points, contours, features, letters, and words. A unique feature representation is generated bottom-up from the image using statistical dependences between letters and features. Ratings for partially formed words are computed using a stack algorithm and a lexicon represented as a trie. Several novel techniques for low- and intermediate-level processing for cursive script are described, including heuristics for reference line finding, letter segmentation based on detecting local minima along the lower contour and areas with low vertical profiles, simultaneous encoding of contours and their topological relationships, extracting features, and finding shape-oriented events. Experiments demonstrating the performance of the system are also described
The state of the art in handwriting recognition, especially in
cursive word recognition, is surveyed, and some basic notions are
reviewed in the field of picture recognition, particularly, line image
recognition. The usefulness of `regular' versus `singular' classes of
features is stressed. These notions are applied to obtain a graph, G
, representing a line image, and also to find an `axis' as the
regular part of G . The complements to G of the axis
are the `tarsi', singular parts of G , which correspond to
informative features of a cursive word. A segmentation of the graph is
obtained, giving a symbolic description chain (SDC). Using one or more
as robust anchors, possible words in a list of words are selected.
Candidate words are examined to see if the other letters fit the rest of
the SDC. Good results are obtained for clean images of words written by
several persons
Stroke versus character-based recognition of on-line connected cursive script, in From Pixels to Features III: Frontiers in Int Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 01/26/15. For personal use only. Handwriting Recognition
- L R B Schomaker
- H L Teulings
L. R. B. Schomaker and H. L. Teulings, Stroke versus character-based recognition of on-line connected cursive script, in From Pixels to Features III: Frontiers in Int. J. Patt. Recogn. Artif. Intell. 2004.18:1157-1171. Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 01/26/15. For personal use only. Handwriting Recognition, eds. S. Impedovo and J. C. Simon (Elsevier, Amsterdam, 1992), pp. 315–325.