Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images

There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author's identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this paper, we present a deep adaptive learning method for writer identification based on single-word images using multi-task learning. An auxiliary task is added to the training process to enforce the emergence of reusable features. Our proposed method transfers the benefits of the learned features of a convolutional neural network from an auxiliary task such as explicit content recognition to the main task of writer identification in a single procedure. Specifically, we propose a new adaptive convolutional layer to exploit the learned deep features. A multi-task neural network with one or several adaptive convolutional layers is trained end-to-end, to exploit robust generic features for a specific main task, i.e., writer identification. Three auxiliary tasks, corresponding to three explicit attributes of handwritten word images (lexical content, word length and character attributes), are evaluated. Experimental results on two benchmark datasets show that the proposed deep adaptive learning method can improve the performance of writer identification based on single-word images, compared to non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio

Novel geometric features for off-line writer identification

Writer identification is an important field in forensic document examination. Typically, a writer identification system consists of two main steps: feature extraction and matching and the performance depends significantly on the feature extraction step. In this paper, we propose a set of novel geometrical features that are able to characterize different writers. These features include direction, curvature, and tortuosity. We also propose an improvement of the edge-based directional and chain code-based features. The proposed methods are applicable to Arabic and English handwriting. We have also studied several methods for computing the distance between feature vectors when comparing two writers. Evaluation of the methods is performed using both the IAM handwriting database and the QUWI database for each individual feature reaching Top1 identification rates of 82 and 87 % in those two datasets, respectively. The accuracies achieved by Kernel Discriminant Analysis (KDA) are significantly higher than those observed before feature-level writer identification was implemented. The results demonstrate the effectiveness of the improved versions of both chain-code features and edge-based directional features.Qatar National Research Fund through the National Priority Research Program (NPRP) No. 09-864-1-128Scopu

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

Preprocessing Algorithm for Deciphering Historical Inscriptions Using String Metric

The article presents the improvements in the preprocessing part of the deciphering method (shortly preprocessing algorithm) for historical inscriptions of unknown origin. Glyphs used in historical inscriptions changed through time; therefore, various versions of the same script may contain different glyphs for each grapheme. The purpose of the preprocessing algorithm is reducing the running time of the deciphering process by filtering out the less probable interpretations of the examined inscription. However, the first version of the preprocessing algorithm leads incorrect outcome or no result in the output in certain cases. Therefore, its improved version was developed to find the most similar words in the dictionary by relaying the search conditions more accurately, but still computationally effectively. Moreover, a sophisticated similarity metric used to determine the possible meaning of the unknown inscription is introduced. The results of the evaluations are also detailed

Text-independent writer identification using convolutional neural network

The text-independent approach to writer identification does not require the writer to write some predetermined text. Previous research on text-independent writer identification has been based on identifying writer-specific features designed by experts. However, in the last decade, deep learning methods have been successfully applied to learn features from data automatically. We propose here an end-to-end deep-learning method for text-independent writer identification that does not require prior identification of features. A Convolutional Neural Network (CNN) is trained initially to extract local features, which represent characteristics of individual handwriting in the whole character images and their sub-regions. Randomly sampled tuples of images from the training set are used to train the CNN and aggregate the extracted local features of images from the tuples to form global features. For every training epoch, the process of randomly sampling tuples is repeated, which is equivalent to a large number of training patterns being prepared for training the CNN for text-independent writer identification. We conducted experiments on the JEITA-HP database of offline handwritten Japanese character patterns. With 200 characters, our method achieved an accuracy of 99.97% to classify 100 writers. Even when using 50 characters for 100 writers or 100 characters for 400 writers, our method achieved accuracy levels of 92.80% or 93.82%, respectively. We conducted further experiments on the Firemaker and IAM databases of offline handwritten English text. Using only one page per writer to train, our method achieved over 91.81% accuracy to classify 900 writers. Overall, we achieved a better performance than the previously published best result based on handcrafted features and clustering algorithms, which demonstrates the effectiveness of our method for handwritten English text also

Wrighting in Englesh is harde: An error analysis of Norwegian pupils' L2 English spelling errors

Denne masteroppgaven i engelsk lingvistikk er en skrivefeilsanalyse som undersøker engelske skrivefeil produsert av norske elever i 10. klasse på ungdomsskolen og 11. klasse på videregående (vanligvis kjent som VG1). Denne oppgaven tar i bruk seks overordnede kategorier for å skille mellom ulike typer skrivefeil for å identifisere hvilke skrivefeil norske elever produserer, og videre hvorfor de tilsynelatende produserer disse skrivefeilene. Datamaterialet er hentet fra det digitale korpuset CORYL (en samling av nasjonale prøver i engelsk fra 2004/2005), samt et kontrollkorpus (en samling av engelske heldagsprøver) som ble utviklet i sammenheng med dette prosjektet i løpet av høsten 2020 og våren 2021. Ettersom tekstene i CORYL er skrevet for hånd, og tekstene i kontrollkorpuset er skrevet på PC, undersøker denne oppgaven også hvordan bruken av PC påvirker elevenes skrivefeil. Funnene i oppgaven tyder på at det er flere elementer som ligger bak elevenes engelske skrivefeil. Den tydeligste tendensen er at elevene bruker fonologiske strategier når de skal utlede hvordan et ukjent ord skal staves, og at dette igjen kan føre til at elevene staver ord feil da det ikke er en en-til-en korrespondanse mellom uttale og stavemåte i engelsk. Funnene tyder også på at elevene har et overforbruk av stavemåter som de gjerne anser å være ‘typisk engelsk’. Bruken av PC ser ut til å føre til at antall skrivefeil synker, og det antas at stavekontrollen i tekstbehandlingsprogrammet brukt av elevene har bidratt til nedgangen i antall skrivefeil. I tillegg viser funnene at visse typer skrivefeil har høyere forekomst, samt at nye typer skrivefeil oppstår, når elevene skriver på PC. Ettersom funnene fra denne masteroppgaven er basert på et mindre datagrunnlag, er norske elevers engelske skrivefeil et felt som må forskes på ytterligere før sikre konklusjoner kan trekkes.Engelsk mastergradsoppgaveENG350MAHF-ENGMAHF-LÆF

Graph-based word spotting by inexact matching techniques

Al llarg d'aquest projecte s'ha desenvolupat un nou mètode de word spotting (localització de paraules) en què es té molt en compte l'estructura de les paraules a buscar. Aquestes tècniques consisteixen a trobar paraules escrites a mà, a partir d'un exemple. La tècnica presentada s'ha desenvolupat per utilitzar-la en documents antics. Seguidament, es presenta una indexació per tal d'accelerar el procés de cerca. Aquesta indexació consisteix a trobar ràpidament un conjunt de candidats on aplicar tècniques de word spotting en grans col·leccions de documents. Finalment, es mostra un exemple d'aplicació de les tècniques desenvolupades en una aplicació per a dispositius Android.A lo largo del proyecto se ha desarrollado un nuevo método de word spotting (localización de palabras) en el cual se tiene muy en consideración la estructura de las palabras a buscar. Estas técnicas consisten en encontrar palabras escritas a mano partiendo de un ejemplo. La técnica presentada se ha desarrollado utilizándola en documentos antiguos. Seguidamente, se presenta una indexación con el objetivo de acelerar el proceso de búsqueda. Esta indexación consiste en encontrar rápidamente un conjunto de candidatos donde aplicar técnicas de word spotting en grandes colecciones de documentos. Finalmente, se muestra un ejemplo de aplicación de la técnica desarrollada en una aplicación para dispositivos Android.Along this project a new method for word spotting (location of words) has been developed. This method has in mind the structure of the words to search. These techniques consist in finding handwritten words from a given example. The presented technique has been meant to be used in old documents. Afterwards an indexation process is presented to speed up the search step. This indexation is used to find a set of candidates in large document collections in order to apply word spotting techniques. Finally, an example application of the developed techniques is proposed for Android devices