Writing Order Recovery from Off-Line Handwriting by Graph Traversal (original) (raw)

Temporal Order Recovery of the Scanned Handwriting

2009 10th International Conference on Document Analysis and Recognition, 2009

In this paper, we present a new approach to the temporal order restoration of the off-line handwriting. After the preprocessing steps of the word image, a suitable algorithm makes it possible to segment its skeleton in three types of strokes. After that, we developed a genetic algorithm GA in order to optimize the best trajectory of these segments. The repetition of a segment will be studied in a secondary algorithm so that we do not disturb the GA operations. The techniques used in GA are the selection, crossover and the mutation. The fitness function value depends on right-left direction (direction of the Arab writing), the segments repetition and angular deviation on the crossing of the occlusion stroke. To validate our approach, we tested it on the On/Off LMCA dual Arabic handwriting, the Latin IRONOFF and the off-line IFN/ENIT datasets.

Writing Order Recovery in Complex and Long Static Handwriting

International Journal of Interactive Multimedia and Artificial Intelligence, 2021

The order in which the trajectory is executed is a powerful source of information for recognizers. However, there is still no general approach for recovering the trajectory of complex and long handwriting from static images. Complex specimens can result in multiple pen-downs and in a high number of trajectory crossings yielding agglomerations of pixels (also known as clusters). While the scientific literature describes a wide range of approaches for recovering the writing order in handwriting, these approaches nevertheless lack a common evaluation metric. In this paper, we introduce a new system to estimate the order recovery of thinned static trajectories, which allows to effectively resolve the clusters and select the order of the executed pendowns. We evaluate how knowing the starting points of the pen-downs affects the quality of the recovered writing. Once the stability and sensitivity of the system is analyzed, we describe a series of experiments with three publicly available databases, showing competitive results in all cases. We expect the proposed system, whose code is made publicly available to the research community, to reduce potential confusion when the order of complex trajectories are recovered, and this will in turn make the trajectories recovered to be viable for further applications, such as velocity estimation.

2009 10th International Conference on Document Analysis and Recognition Temporal Order Recovery of the Scanned Handwriting

2012

Recovering dynamic information from static handwriting

1993

It is generally agreed that the advantage of on-line character recognition methods with respect to off-line ones mostly relies on the availability of dynamic information. This mainly concerns the order in which the strokes forming characters have been drawn. In this paper we present and discuss a method which attempts, in the off-line case, to recover part of the lost script dynamics. The method makes it possible to reconstruct one of the most likely trajectories followed by the writer while drawing characters.

Pen Trace Reconstruction with Skeleton Representation of a Handwritten Text Image

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

This paper presents a new approach for the pen trace reconstruction task in handwritten text images. The proposed approach is based on the skeleton representation of a binary image which provides sufficient information about the pen movement trajectory during the writing. As such, the initial problem can be viewed as a problem of constructing a specific walk in the skeleton graph covering all its edges. The proposed approach consists of splitting the graph into structural elements and estimating the pen movement direction in each of them with the resulting trace being composed of the traces of these elements.

Recognition-directed recovering of temporal information from handwriting images

Pattern Recognition Letters, 2005

This paper analyses a handwriting recognition system for offline cursive words based on HMMs. It compares two approaches for transforming offline handwriting available as two-dimensional images into one-dimensional input signals that can be processed by HMMs. In the first approach, a left-right scan of the word is performed resulting in a sequence of feature vectors. In the second approach, a more subtle process attempts to recover the temporal order of the strokes that form words as they were written. This is accomplished by a graph model that generates a set of paths, each path being a possible temporal order of the handwriting. The recognition process then selects the most likely temporal stroke order based on knowledge that has been acquired from a large set of handwriting samples for which the temporal information was available. We show experimentally that such an offline recognition system using the recovered temporal order can achieve recognition performances that are much better than those obtained with the simple left-right order, and that come close to those of an online recognition system. We have been able to assess the ordering quality of handwriting when comparing true ordering and recovered one, and we also analyze the situations where offline and online information differ and what the consequences are on the recognition performances. For these evaluations, we have used about 30,000 words from the IRONOFF database that features both the online signal and offline signal for each word.

AN EFFICIENT METHOD FOR ONLINE CURSIVE HANDWRITING STROKES REORDERING

International Journal of Pattern Recognition and Artificial Intelligence, 2004

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.

Techniques for static handwriting trajectory recovery: a survey

2010

On-line handwriting recognition systems are usually better than their off-line counterparts thanks to the accessibility to dynamic information such as stroke order, velocity, acceleration, and pressure. Whilst the exact value of velocity as well as acceleration or pressure is unlikely to be recoverable, the temporal order of the strokes or the pen trajectory is shown to be more promising for recovery. The experimental results reported in the literature suggest that the recovered pen trajectory actually improves the off-line recognition accuracy. This survey presents an overview and discussion of pen trajectory recovery methods developed to date.

Techniques for static handwriting trajectory recovery

Proceedings of the 8th IAPR International Workshop on Document Analysis Systems - DAS '10, 2010

Segmentation and reconstruction of on-line handwritten scripts

Pattern Recognition, 1998

On-line handwritten scripts consist of sequences of components that are pen tip traces from pen-down to pen-up positions. This paper presents a segmentation and reconstruction procedure which segments components of a script into sequences of static strokes, and then reconstructs the script from these sequences. The segmentation is based on the extrema of curvature and inection points in individual components. The static strokes are derived from the delta log-normal model of handwriting generation and are used in component representation and reconstruction. The performance of the procedure is measured in terms of root mean square reconstruction error and data compression rate.

Writing Order Recovery from Off-Line Handwriting by Graph Traversal (original) (raw)

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