Automatic handwriter identification using advanced machine learning

Handwriter identification a challenging problem especially for forensic investigation. This topic has received significant attention from the research community and several handwriter identification systems were developed for various applications including forensic science, document analysis and investigation of the historical documents. This work is part of an investigation to develop new tools and methods for Arabic palaeography, which is is the study of handwritten material, particularly ancient manuscripts with missing writers, dates, and/or places. In particular, the main aim of this research project is to investigate and develop new techniques and algorithms for the classification and analysis of ancient handwritten documents to support palaeographic studies. Three contributions were proposed in this research. The first is concerned with the development of a text line extraction algorithm on colour and greyscale historical manuscripts. The idea uses a modified bilateral filtering approach to adaptively smooth the images while still preserving the edges through a nonlinear combination of neighboring image values. The proposed algorithm aims to compute a median and a separating seam and has been validated to deal with both greyscale and colour historical documents using different datasets. The results obtained suggest that our proposed technique yields attractive results when compared against a few similar algorithms. The second contribution proposes to deploy a combination of Oriented Basic Image features and the concept of graphemes codebook in order to improve the recognition performances. The proposed algorithm is capable to effectively extract the most distinguishing handwriter’s patterns. The idea consists of judiciously combining a multiscale feature extraction with the concept of grapheme to allow for the extraction of several discriminating features such as handwriting curvature, direction, wrinkliness and various edge-based features. The technique was validated for identifying handwriters using both Arabic and English writings captured as scanned images using the IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting. The results obtained clearly demonstrate the effectiveness of the proposed method when compared against some similar techniques. The third contribution is concerned with an offline handwriter identification approach based on the convolutional neural network technology. At the first stage, the Alex-Net architecture was employed to learn image features (handwritten scripts) and the features obtained from the fully connected layers of the model. Then, a Support vector machine classifier is deployed to classify the writing styles of the various handwriters. In this way, the test scripts can be classified by the CNN training model for further classification. The proposed approach was evaluated based on Arabic Historical datasets; Islamic Heritage Project (IHP) and Qatar National Library (QNL). The obtained results demonstrated that the proposed model achieved superior performances when compared to some similar method

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

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Biometrics Writer Recognition for Arabic language: Analysis and Classification techniques using Subwords Features

Handwritten text in any language is believed to convey a great deal of information about writers’ personality and identity. Indeed, handwritten signature has long been accepted as an authentication of the writer’s physical stamp on financial and legal deals as well official/personal documents and works of art. Handwritten documents are frequently used as evidences in forensic tasks. Handwriting skills is learnt and developed from the early schooling stages. Research interest in behavioral biometrics was the main driving force behind the growth in research into Writer Identification (WI) from handwritten text, but recent rise in terrorism associated with extreme religious ideologies spreading primarily, but not exclusively, from the middle-east has led to a surge of interest in WI from handwritten text in Arabic and similar languages. This thesis is the main outcome of extensive research investigations conducted with the aim of developing an automatic identification of a person from handwritten Arabic text samples. My motivations and interests, as an Iraqi researcher, emanate from my multi-faceted desires to provide scientific support for my people in their fight against terrorism by providing forensic evidences, and as contribute to the ongoing digitization of the Iraqi National archive as well as the wealth of religious and historical archives in Iraq and the middle-east. Good knowledge of the underlying language is invaluable in this project. Despite the rising interest in this recognition modality worldwide, Arabic writer identification has not been addressed as extensively as Latin writer identification. However, in recent years some new Arabic writer identification approaches have been proposed some of which are reviewed in this thesis. Arabic is a cursive language when handwritten. This means that each and every writer in this language develops some unique features that could demonstrate writer’s habits and style. These habits and styles are considered as unique WI features and determining factors. Existing dominating approaches to WI are based on recognizing handwriting habits/styles are embedded in certain parts/components of the written texts. Although the appearance of these components within long text contain rich information and clues to writer identity, the most common approaches to WI in Arabic in the literature are based on features extracted from paragraph(s), line(s), word(s), character(s), and/or a part of a character. Generally, Arabic words are made up of one or more subwords at the end of each; there is a connected stroke with a certain style of which seem to be most representative of writers habits. Another feature of Arabic writing is to do with diacritics that are added to written words/subwords, to add meaning and pronunciation. Subwords are more frequent in written Arabic text and appear as part of several different words or as full individual words. Thus, we propose a new innovative approach based on a seemingly plausible hypothesis that subwords based WI yields significant increase in accuracy over existing approaches. The thesis most significant contributions can be summarized as follows: - Developed a high performing segmentation of scanned text images, that combines threshold based binarisation, morphological operation and active shape model. - Defined digital measures and formed a 15-dimensional feature vectors representations of subwords that implicitly cover its diacritics and strokes. A pilot study that incrementally added features according to writer discriminating power. This reduced subwords feature vector dimension to 8, two of which were modelled as time series. - For the dependent 8-dimensional WI scheme, we identify the best performing set of subwords (best 22 subwords out of 49 then followed by best 11 out of these 22 subwords). - We established the validity of our hypothesis for different versions of subwords based WI schemes by providing empirical evidence when testing on a number of existing text dependent and in text-dependent databases plus a simulated text-in text-dependent DB. The text-dependent scenario results exhibited possible present of the Doddington Zoo phenomena. - The final optimal subword based WI scheme, not only removes the need to include diacritics as part of the subword but also demonstrating that including diacritics within subwords impairs the WI discriminating power of subwords. This should not be taken to discredit research that are based on diacritics based WI. Also in this subword body (without diacritics) base WI scheme, resulted in eliminating the presence of Doddington Zoo effect. - Finally, a significant but un-intended consequence of using subwords for WI is that there is no difference between a text-independent scenario and text-dependent one. In fact, we shall demonstrate that the text-dependent database of the 27-words can be used to simulate the testing of the scheme for an in text-dependent database without the need to record such a DB. Finally, we discussed ways of optimising the performance of our last scheme by considering possible ways of complementing our scheme using the addition of various image texture analysis features to be extracted from subwords, lines, paragraphs or entire file of the scabbed image. These included LBP and Gabor Filter. We also suggested the possible addition of few more features

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

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications