Chinese calligraphy: character style recognition based on full-page document

Calligraphy plays a very important role in the history of China. From ancient times to modern times, the beauty of calligraphy has been passed down to the present. Different calligraphy styles and structures have made calligraphy a beauty and embodiment in the field of writing. However, the recognition of calligraphy style and fonts has always been a blank in the computer field. The structural complexity of different calligraphy also brings a lot of challenges to the recognition technology of computers. In my research, I mainly discussed some of the main recognition techniques and some popular machine learning algorithms in this field for more than 20 years, trying to find a new method of Chinese calligraphy styles recognition and exploring its feasibility. In our research, we searched for research papers 20 years ago. Most of the results are about the content recognition of modern Chinese characters. At first, we analyze the development of Chinese characters and the basic Chinese character theory. In the analysis of the current recognition of Chinese characters (including handwriting online and offline) in the computer field, it is more important to analyze various algorithms and results, and to analyze how to use the experimental data, besides how they construct the data set used for their test. The research on the method of image processing based on Chinese calligraphy works is very limited, and the data collection for calligraphy test is very limited also. The test of dataset that used between different recognition technologies is also very different. However, it has far-reaching significance for inheriting and carrying forward the traditional Chinese culture. It is very necessary to develop and promote the recognition of Chinese characters by means of computer tecnchque. In the current application field, the font recognition of Chinese calligraphy can effectively help the library administrators to identify the problem of the classification of the copybook, thus avoiding the recognition of the calligraphy font which is difficult to perform manually only through subjective experience. In the past 10 years of technology, some techniques for the recognition of single Chinese calligraphy fonts have been given. Most of them are the pre-processing of calligraphy characters, the extraction of stroke primitives, the extraction of style features, and the final classification of machine learning. The probability of the classification of the calligraphy works. Such technical requirements are very large for complex Chinese characters, the result of splitting and recognition is very large, and it is difficult to accurately divide many complex font results. As a result, the recognition rate is low, or the accuracy of recognition of a specific word is high, but the overall font recognition accuracy is low. We understand that Chinese calligraphy is a certain research value. In the field of recognition, many research papers on the analysis of Chinese calligraphy are based on the study of calligraphy and stroke. However, we have proposed a new method for dealing with font recognition. The recognition technology is based on the whole page of the document. It is studied in three steps: the first step is to use Fourier transform and some Chinese calligraphy images and analyze the results. The second is that CNN is based on different data sets to get some results. Finally, we made some improvements to the CNN structure. The experimental results of the thesis show that the full-page documents recognition method proposed can achieve high accuracy with the support of CNN technology, and can effectively identify the different styles of Chinese calligraphy in 5 styles. Compared with the traditional analysis methods, our experimental results show that the method based on the full-page document is feasible, avoiding the cumbersome font segmentation problem. This is more efficient and more accurate

A framework for ancient and machine-printed manuscripts categorization

Document image understanding (DIU) has attracted a lot of attention and became an of active fields of research. Although, the ultimate goal of DIU is extracting textual information of a document image, many steps are involved in a such a process such as categorization, segmentation and layout analysis. All of these steps are needed in order to obtain an accurate result from character recognition or word recognition of a document image. One of the important steps in DIU is document image categorization (DIC) that is needed in many situations such as document image written or printed in more than one script, font or language. This step provides useful information for recognition system and helps in reducing its error by allowing to incorporate a category-specific Optical Character Recognition (OCR) system or word recognition (WR) system. This research focuses on the problem of DIC in different categories of scripts, styles and languages and establishes a framework for flexible representation and feature extraction that can be adapted to many DIC problem. The current methods for DIC have many limitations and drawbacks that restrict the practical usage of these methods. We proposed an efficient framework for categorization of document image based on patch representation and Non-negative Matrix Factorization (NMF). This framework is flexible and can be adapted to different categorization problem. Many methods exist for script identification of document image but few of them addressed the problem in handwritten manuscripts and they have many limitations and drawbacks. Therefore, our first goal is to introduce a novel method for script identification of ancient manuscripts. The proposed method is based on patch representation in which the patches are extracted using skeleton map of a document images. This representation overcomes the limitation of the current methods about the fixed level of layout. The proposed feature extraction scheme based on Projective Non-negative Matrix Factorization (PNMF) is robust against noise and handwriting variation and can be used for different scripts. The proposed method has higher performance compared to state of the art methods and can be applied to different levels of layout. The current methods for font (style) identification are mostly proposed to be applied on machine-printed document image and many of them can only be used for a specific level of layout. Therefore, we proposed new method for font and style identification of printed and handwritten manuscripts based on patch representation and Non-negative Matrix Tri-Factorization (NMTF). The images are represented by overlapping patches obtained from the foreground pixels. The position of these patches are set based on skeleton map to reduce the number of patches. Non-Negative Matrix Tri-Factorization is used to learn bases from each fonts (style) and then these bases are used to classify a new image based on minimum representation error. The proposed method can easily be extended to new fonts as the bases for each font are learned separately from the other fonts. This method is tested on two datasets of machine-printed and ancient manuscript and the results confirmed its performance compared to the state of the art methods. Finally, we proposed a novel method for language identification of printed and handwritten manuscripts based on patch representation and Non-negative Matrix Tri-Factorization (NMTF). The current methods for language identification are based on textual data obtained by OCR engine or images data through coding and comparing with textual data. The OCR based method needs lots of processing and the current image based method are not applicable to cursive scripts such as Arabic. In this work we introduced a new method for language identification of machine-printed and handwritten manuscripts based on patch representation and NMTF. The patch representation provides the component of the Arabic script (letters) that can not be extracted simply by segmentation methods. Then NMTF is used for dictionary learning and generating codebooks that will be used to represent document image with a histogram. The proposed method is tested on two datasets of machine-printed and handwritten manuscripts and compared to n-gram features (text-based), texture features and codebook features (imagebased) to validate the performance. The above proposed methods are robust against variation in handwritings, changes in the font (handwriting style) and presence of degradation and are flexible that can be used to various levels of layout (from a textline to paragraph). The methods in this research have been tested on datasets of handwritten and machine-printed manuscripts and compared to state-of-the-art methods. All of the evaluations show the efficiency, robustness and flexibility of the proposed methods for categorization of document image. As mentioned before the proposed strategies provide a framework for efficient and flexible representation and feature extraction for document image categorization. This frame work can be applied to different levels of layout, the information from different levels of layout can be merged and mixed and this framework can be extended to more complex situations and different tasks

Timor Leste Tais Motif Recognition Using Wavelet and Backpropagation

Timor Leste is a new country of the 21st century in Southeast Asia that has a diverse culture. Tais Timor Leste has a high historical value as well as cultural identity. It is also one of the cultural heritages of Timor Leste. Tais Timor has its own characteristics and meanings in every motif, but there are still many communities of Timor Leste as well as foreign tourists who do not know the variety of the motif. Therefore, this study aimed to establish the system recognition of Tais Timor motif through the image based on the type of motif. The wavelet transform is used in the process of feature extraction and image decomposition to obtain coefficient values of which the value of energy and entropy will then be calculated. For the recognition of Tais Timor motif, backpropagation algorithm was used. This application is built using MATLAB programming language. The analysis and testing of these studies show that the accuracy of recognition of Tais Timor motif with 4 testing parameters got recognition accuracy and presentation of 80%. Thus the motif used can be identified by using both wavelet transform and backpropagation algorithm

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

End-Shape Analysis for Automatic Segmentation of Arabic Handwritten Texts

Word segmentation is an important task for many methods that are related to document understanding especially word spotting and word recognition. Several approaches of word segmentation have been proposed for Latin-based languages while a few of them have been introduced for Arabic texts. The fact that Arabic writing is cursive by nature and unconstrained with no clear boundaries between the words makes the processing of Arabic handwritten text a more challenging problem. In this thesis, the design and implementation of an End-Shape Letter (ESL) based segmentation system for Arabic handwritten text is presented. This incorporates four novel aspects: (i) removal of secondary components, (ii) baseline estimation, (iii) ESL recognition, and (iv) the creation of a new off-line CENPARMI ESL database. Arabic texts include small connected components, also called secondary components. Removing these components can improve the performance of several systems such as baseline estimation. Thus, a robust method to remove secondary components that takes into consideration the challenges in the Arabic handwriting is introduced. The methods reconstruct the image based on some criteria. The results of this method were subsequently compared with those of two other methods that used the same database. The results show that the proposed method is effective. Baseline estimation is a challenging task for Arabic texts since it includes ligature, overlapping, and secondary components. Therefore, we propose a learning-based approach that addresses these challenges. Our method analyzes the image and extracts baseline dependent features. Then, the baseline is estimated using a classifier. Algorithms dealing with text segmentation usually analyze the gaps between connected components. These algorithms are based on metric calculation, finding threshold, and/or gap classification. We use two well-known metrics: bounding box and convex hull to test metric-based method on Arabic handwritten texts, and to include this technique in our approach. To determine the threshold, an unsupervised learning approach, known as the Gaussian Mixture Model, is used. Our ESL-based segmentation approach extracts the final letter of a word using rule-based technique and recognizes these letters using the implemented ESL classifier. To demonstrate the benefit of text segmentation, a holistic word spotting system is implemented. For this system, a word recognition system is implemented. A series of experiments with different sets of features are conducted. The system shows promising results