Recognition of handwritten Arabic characters

The subject of handwritten character recognition has been receiving considerable attention in recent years due to the increased dependence on computers. Several methods for recognizing Latin, Chinese as well as Kanji characters have been proposed. However, work on recognition of Arabic characters has been relatively sparse. Techniques developed for recognizing characters in other languages can not be used for Arabic since the nature of Arabic characters is different. The shape of a character is a function of its location within a word where each character can have two to four different forms. Most of the techniques proposed to date for recognizing Arabic characters have relied on structural and topographic approaches. This thesis introduces a decision-theoretic approach to solve the problem. The proposed method involves, as a first step, digitization of the segmented character. The secondary part of the character (dots and zigzags) are then isolated and identified separately thereby reducing the recognition issue to a 20 class problem or less for each of the character forms. The moments of the horizontal and vertical projections of the remaining primary characters are calculated and normalized with respect to the zero order moment. Simple measures of shape are obtained from the normalized moments and incorporated into a feature vector. Classification is accomplished using quadratic discriminant functions. The approach was evaluated using isolated, handwritten characters from a data base established for this purpose. The classification rates varied from 97.5% to 100% depending on the form of the characters. These results indicate that the technique offers significantly better classification rates in comparison with existing methods

SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-world Verification

An open research problem in automatic signature verification is the skilled forgery attacks. However, the skilled forgeries are very difficult to acquire for representation learning. To tackle this issue, this paper proposes to learn dynamic signature representations through ranking synthesized signatures. First, a neuromotor inspired signature synthesis method is proposed to synthesize signatures with different distortion levels for any template signature. Then, given the templates, we construct a lightweight one-dimensional convolutional network to learn to rank the synthesized samples, and directly optimize the average precision of the ranking to exploit relative and fine-grained signature similarities. Finally, after training, fixed-length representations can be extracted from dynamic signatures of variable lengths for verification. One highlight of our method is that it requires neither skilled nor random forgeries for training, yet it surpasses the state-of-the-art by a large margin on two public benchmarks.Comment: To appear in AAAI 202

Adaptive combinations of classifiers with application to on-line handwritten character recognition

Classifier combining is an effective way of improving classification performance. User adaptation is clearly another valid approach for improving performance in a user-dependent system, and even though adaptation is usually performed on the classifier level, also adaptive committees can be very effective. Adaptive committees have the distinct ability of performing adaptation without detailed knowledge of the classifiers. Adaptation can therefore be used even with classification systems that intrinsically are not suited for adaptation, whether that be due to lack of access to the workings of the classifier or simply a classification scheme not suitable for continuous learning. This thesis proposes methods for adaptive combination of classifiers in the setting of on-line handwritten character recognition. The focal part of the work introduces adaptive classifier combination schemes, of which the two most prominent ones are the Dynamically Expanding Context (DEC) committee and the Class-Confidence Critic Combining (CCCC) committee. Both have been shown to be capable of successful adaptation to the user in the task of on-line handwritten character recognition. Particularly the highly modular CCCC framework has shown impressive performance also in a doubly-adaptive setting of combining adaptive classifiers by using an adaptive committee. In support of this main topic of the thesis, some discussion on a methodology for deducing correct character labeling from user actions is presented. Proper labeling is paramount for effective adaptation, and deducing the labels from the user's actions is necessary to perform adaptation transparently to the user. In that way, the user does not need to give explicit feedback on the correctness of the recognition results. Also, an overview is presented of adaptive classification methods for single-classifier adaptation in handwritten character recognition developed at the Laboratory of Computer and Information Science of the Helsinki University of Technology, CIS-HCR. Classifiers based on the CIS-HCR system have been used in the adaptive committee experiments as both member classifiers and to provide a reference level. Finally, two distinct approaches for improving the performance of committee classifiers further are discussed. Firstly, methods for committee rejection are presented and evaluated. Secondly, measures of classifier diversity for classifier selection, based on the concept of diversity of errors, are presented and evaluated. The topic of this thesis hence covers three important aspects of pattern recognition: on-line adaptation, combining classifiers, and a practical evaluation setting of handwritten character recognition. A novel approach combining these three core ideas has been developed and is presented in the introductory text and the included publications. To reiterate, the main contributions of this thesis are: 1) introduction of novel adaptive committee classification methods, 2) introduction of novel methods for measuring classifier diversity, 3) presentation of some methods for implementing committee rejection, 4) discussion and introduction of a method for effective label deduction from on-line user actions, and as a side-product, 5) an overview of the CIS-HCR adaptive on-line handwritten character recognition system.Luokittimien yhdistäminen komitealuokittimella on tehokas keino luokitustarkkuuden parantamiseen. Laskentatehon jatkuva kasvu tekee myös useiden luokittimien yhtäaikaisesta käytöstä yhä varteenotettavamman vaihtoehdon. Järjestelmän adaptoituminen (mukautuminen) käyttäjään on toinen hyvä keino käyttäjäriippumattoman järjestelmän tarkkuuden parantantamiseksi. Vaikka adaptaatio yleensä toteutetaan luokittimen tasolla, myös adaptiiviset komitealuokittimet voivat olla hyvin tehokkaita. Adaptiiviset komiteat voivat adaptoitua ilman yksityiskohtaista tietoa jäsenluokittimista. Adaptaatiota voidaan näin käyttää myös luokittelujärjestelmissä, jotka eivät ole itsessään sopivia adaptaatioon. Adaptaatioon sopimattomuus voi johtua esimerkiksi siitä, että luokittimen totetutusta ei voida muuttaa, tai siitä, että käytetään luokittelumenetelmää, joka ei sovellu jatkuvaan oppimiseen. Tämä väitöskirja käsittelee menetelmiä luokittimien adaptiiviseen yhdistämiseen käyttäen sovelluskohteena käsinkirjoitettujen merkkien on-line-tunnistusta. Keskeisin osa työtä esittelee uusia adaptiivisia luokittimien yhdistämismenetelmiä, joista kaksi huomattavinta ovat Dynamically Expanding Context (DEC) -komitea sekä Class-Confidence Critic Combining (CCCC) -komitea. Molemmat näistä ovat osoittautuneet kykeneviksi tehokkaaseen käyttäjä-adaptaatioon käsinkirjoitettujen merkkien on-line-tunnistuksessa. Erityisesti hyvin modulaarisella CCCC järjestelmällä on saatu hyviä tuloksia myös kaksinkertaisesti adaptiivisessa asetelmassa, jossa yhdistetään adaptiivisia jäsenluokittimia adaptiivisen komitean avulla. Väitöskirjan pääteeman tukena esitetään myös malli ja käytännön esimerkki siitä, miten käyttäjän toimista merkeille voidaan päätellä oikeat luokat. Merkkien todellisen luokan onnistunut päättely on elintärkeää tehokkaalle adaptaatiolle. Jotta adaptaatio voitaisiin suorittaa käyttäjälle läpinäkyvästi, merkkien todelliset luokat on kyettävä päättelemään käyttäjän toimista. Tällä tavalla käyttäjän ei tarvitse antaa suoraa palautetta tunnistustuloksen oikeellisuudesta. Työssä esitetään myös yleiskatsaus Teknillisen korkeakoulun Informaatiotekniikan laboratoriossa kehitettyyn adaptiiviseen käsinkirjoitettujen merkkien tunnistusjärjestelmään. Tähän järjestelmään perustuvia luokittimia on käytetty adaptiivisten komitealuokittimien kokeissa sekä jäsenluokittimina että vertailutasona. Lopuksi esitellään kaksi erillistä menetelmää komitealuokittimen tarkkuuden edelleen parantamiseksi. Näistä ensimmäinen on joukko menetelmiä komitealuokittimen rejektion (hylkäyksen) toteuttamiseksi. Toinen esiteltävä menetelmä on käyttää luokittimien erilaisuuden mittoja jäsenluokittimien valintaa varten. Ehdotetut uudet erilaisuusmitat perustuvat käsitteeseen, jota kutsumme virheiden erilaisuudeksi. Väitöskirjan aihe kattaa kolme hahmontunnistuksen tärkeää osa-aluetta: online-adaptaation, luokittimien yhdistämisen ja käytännön sovellusalana käsinkirjoitettujen merkkien tunnistuksen. Näistä kolmesta lähtökohdasta on kehitetty uudenlainen synteesi, joka esitetään johdantotekstissä sekä liitteenä olevissa julkaisuissa. Tämän väitöskirjan oleellisimmat kontribuutiot ovat siten: 1) uusien adaptiivisten komitealuokittimien esittely, 2) uudenlaisten menetelmien esittely luokittimien erilaisuuden mittaamiseksi, 3) joidenkin komitearejektiomenetelmien esittely, 4) pohdinnan ja erään toteutustavan esittely syötettyjen merkkien todellisen luokan päättelemiseksi käyttäjän toimista, sekä sivutuotteena 5) kattava yleiskatsaus CIS-HCR adaptiiviseen on-line käsinkirjoitettujen merkkien tunnistusjärjestelmään.reviewe

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

HMM word graph based keyword spotting in handwritten document images

[EN] Line-level keyword spotting (KWS) is presented on the basis of frame-level word posterior probabilities. These posteriors are obtained using word graphs derived from the recogni- tion process of a full-fledged handwritten text recognizer based on hidden Markov models and N-gram language models. This approach has several advantages. First, since it uses a holistic, segmentation-free technology, it does not require any kind of word or charac- ter segmentation. Second, the use of language models allows the context of each spotted word to be taken into account, thereby considerably increasing KWS accuracy. And third, the proposed KWS scores are based on true posterior probabilities, taking into account all (or most) possible word segmentations of the input image. These scores are properly bounded and normalized. This mathematically clean formulation lends itself to smooth, threshold-based keyword queries which, in turn, permit comfortable trade-offs between search precision and recall. Experiments are carried out on several historic collections of handwritten text images, as well as a well-known data set of modern English handwrit- ten text. According to the empirical results, the proposed approach achieves KWS results comparable to those obtained with the recently-introduced "BLSTM neural networks KWS" approach and clearly outperform the popular, state-of-the-art "Filler HMM" KWS method. Overall, the results clearly support all the above-claimed advantages of the proposed ap- proach.This work has been partially supported by the Generalitat Valenciana under the Prometeo/2009/014 project grant ALMA-MATER, and through the EU projects: HIMANIS (JPICH programme, Spanish grant Ref. PCIN-2015-068) and READ (Horizon 2020 programme, grant Ref. 674943).Toselli, AH.; Vidal, E.; Romero, V.; Frinken, V. (2016). HMM word graph based keyword spotting in handwritten document images. Information Sciences. 370:497-518. https://doi.org/10.1016/j.ins.2016.07.063S49751837

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