A study of representations for pen based handwriting recognition of tamil characters

In this paper we study the important issue of choosing representations that are suitable for recognizing pen based handwriting of characters in Tamil, a language of India. Four different choices, based on the following set of features are considered: (1) a sequence of directions and curvature; (2) a sequence of angles; (3) Fourier transform coefficients; and (4) wavelet features. We provide arguments in support of the representation using wavelet features. A neural network designed using these features gives excellent accuracy for recognizing Tamil characters

Quantifying scribal behavior : a novel approach to digital paleography

We propose a novel approach for analyzing scribal behavior quantitatively using information about the handwriting of characters. To implement this approach, we develop a computational framework that recovers this information and decomposes the characters into primitives (called strokes) to create a hierarchically structured representation. We then propose a number of intuitive metrics quantifying various facets of scribal behavior, which are derived from the recovered information and character structure. We further propose the use of techniques modeling the generation of handwriting to directly study the changes in writing behavior. We then present a case study in which we use our framework and metrics to analyze the development of four major Indic scripts. We show that our framework and metrics coupled with appropriate statistical methods can provide great insight into scribal behavior by discovering specific trends and phenomena with quantitative methods. We also illustrate the use of handwriting modeling techniques in this context to study the divergence of the Brahmi script into two daughter scripts. We conduct a user study with domain experts to evaluate our framework and salient results from the case study, and we elaborate on the results of this evaluation. Finally, we present our conclusions and discuss the limitations of our research along with future work that needs to be done

Disentangling Writer and Character Styles for Handwriting Generation

Training machines to synthesize diverse handwritings is an intriguing task. Recently, RNN-based methods have been proposed to generate stylized online Chinese characters. However, these methods mainly focus on capturing a person's overall writing style, neglecting subtle style inconsistencies between characters written by the same person. For example, while a person's handwriting typically exhibits general uniformity (e.g., glyph slant and aspect ratios), there are still small style variations in finer details (e.g., stroke length and curvature) of characters. In light of this, we propose to disentangle the style representations at both writer and character levels from individual handwritings to synthesize realistic stylized online handwritten characters. Specifically, we present the style-disentangled Transformer (SDT), which employs two complementary contrastive objectives to extract the style commonalities of reference samples and capture the detailed style patterns of each sample, respectively. Extensive experiments on various language scripts demonstrate the effectiveness of SDT. Notably, our empirical findings reveal that the two learned style representations provide information at different frequency magnitudes, underscoring the importance of separate style extraction. Our source code is public at: https://github.com/dailenson/SDT.Comment: accepted by CVPR 2023. Source code: https://github.com/dailenson/SD

Incorporation of relational information in feature representation for online handwriting recognition of Arabic characters

Interest in online handwriting recognition is increasing due to market demand for both improved performance and for extended supporting scripts for digital devices. Robust handwriting recognition of complex patterns of arbitrary scale, orientation and location is elusive to date because reaching a target recognition rate is not trivial for most of the applications in this field. Cursive scripts such as Arabic and Persian with complex character shapes make the recognition task even more difficult. Challenges in the discrimination capability of handwriting recognition systems depend heavily on the effectiveness of the features used to represent the data, the types of classifiers deployed and inclusive databases used for learning and recognition which cover variations in writing styles that introduce natural deformations in character shapes. This thesis aims to improve the efficiency of online recognition systems for Persian and Arabic characters by presenting new formal feature representations, algorithms, and a comprehensive database for online Arabic characters. The thesis contains the development of the first public collection of online handwritten data for the Arabic complete-shape character set. New ideas for incorporating relational information in a feature representation for this type of data are presented. The proposed techniques are computationally efficient and provide compact, yet representative, feature vectors. For the first time, a hybrid classifier is used for recognition of online Arabic complete-shape characters based on the idea of decomposing the input data into variables representing factors of the complete-shape characters and the combined use of the Bayesian network inference and support vector machines. We advocate the usefulness and practicality of the features and recognition methods with respect to the recognition of conventional metrics, such as accuracy and timeliness, as well as unconventional metrics. In particular, we evaluate a feature representation for different character class instances by its level of separation in the feature space. Our evaluation results for the available databases and for our own database of the characters' main shapes confirm a higher efficiency than previously reported techniques with respect to all metrics analyzed. For the complete-shape characters, our techniques resulted in a unique recognition efficiency comparable with the state-of-the-art results for main shape characters

Relative Positioning of Stroke Based Clustering: A New Approach to On-line Handwritten Devanagari Character Recognition

International audienceIn this paper, we propose a new scheme for Devanagari natural handwritten character recognition. It is primarily based on spatial similarity based stroke clustering. A feature of a stroke consists of a string of pen-tip positions and directions at every pen-tip position along the trajectory. It uses the dynamic time warping (DTW) algorithm to align handwritten strokes with stored stroke templates and determine their similarity. Experiments are carried out with the help of 25 native writers and a recognition rate of approximately 95% is achieved. Our recogniser is robust to a large range of writing style and handles variation in the number of strokes, their order, shapes and sizes and similarities among classes