Touching-Sloping Turkish Handwriten Text Recognition Using K-Nn Classification Method and Lexicon

DergiPark: 245991trakyafbdBu çalışma, henüz tam olarak çözülememiş problem olan birleşik ve eğik Türkçe el yazısı üzerinedir. El yazısı tanımadaki zorluk, kişiden kişiye yazım farklılıkları göstermesi ve harflerin birbirine bitişik yazılmasından kaynaklanmaktadır.Ayrıca Türkçe’nin eklemeli kelime yapısına sahip olması da bu zorluğu arttırmaktadır. Tanıma sisteminde, küçük harflerle yazılmış el yazısı kullanılmıştır. Karakter tanıma aşamasında, sınıflama için k-NN’ den yararlanılmıştır. Kelimelerin tanınmasında, sözlük ve karakterlerin bölütlenmesi birlikte kullanılmıştır. Sözlük kullanımı ile kelime doğrulama aşamasında anlamsız harflerin seçilmesi engellenmiş ve yanlış tanınan kelimelerin düzeltilmesi sağlanmıştır. Çalışmadaki karakter tanıma performansı %90.5 iken kelime tanıma performansı %84 olarak elde edilmiştir. Elde edilen kelime tanıma performansının daha düşük olması çalışmada kullanılan sözcükteki kelime sayısının sınırlı olmasından kaynaklanmaktadırThis study is dealt with Turkish handwritten touching-sloping text recognition. The difficulty of handwritten recognition depends on changing of handwritten person by person and touching-sloping written characters. Also, agglutinative word structure of Turkish language increases difficulty of recognition. It was used lowercase handwritten for recognition system. It was used k-NN for character recognition stage. Character segmentation and lexicon were used together for word recognition. It was blocked choosing incorrect letters using lexicon and corrected recognition of incorrect words. In the study, while performance of character recognition was obtained 90.5%, performance of word recognition was obtained 84%. The lower value of performance of word recognition obtained depends on restricted word in lexicon used for the stud

Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition

Online handwritten Chinese text recognition (OHCTR) is a challenging problem as it involves a large-scale character set, ambiguous segmentation, and variable-length input sequences. In this paper, we exploit the outstanding capability of path signature to translate online pen-tip trajectories into informative signature feature maps using a sliding window-based method, successfully capturing the analytic and geometric properties of pen strokes with strong local invariance and robustness. A multi-spatial-context fully convolutional recurrent network (MCFCRN) is proposed to exploit the multiple spatial contexts from the signature feature maps and generate a prediction sequence while completely avoiding the difficult segmentation problem. Furthermore, an implicit language model is developed to make predictions based on semantic context within a predicting feature sequence, providing a new perspective for incorporating lexicon constraints and prior knowledge about a certain language in the recognition procedure. Experiments on two standard benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with correct rates of 97.10% and 97.15%, respectively, which are significantly better than the best result reported thus far in the literature.Comment: 14 pages, 9 figure

Comparing natural and synthetic training data for off-line cursive handwriting recognition

Abstrac

Couplage d'une vision locale par hmm et globale par RN pour la reconnaissance de mots manuscrits

Colloque avec actes et comité de lecture. internationale.International audienceNous présentons dans cet article une idée de combinaison de modèles à visions locale et globale. Séparément, ces deux types de modèles ont prouvé leurs capacités, et leur combinaison a été exploitée en utilisant les modèles globaux plutôt localement, et les modèles locaux pour synthétiser leurs résultats. Nous proposons une démarche inverse en utilisant les modèles locaux pour normaliser efficacement les images de manière non linéaire, et les modèles globaux pour assurer une cohérence sur l'ensemble de la forme. Les modèles locaux sont de type markovien, et les modèles globaux sont des réseaux de neurones. Le gain de la normalisation markovienne est de l'ordre de 3% par rapport à une normalisation linéaire classique (84.2%). Le gain apporté par la combinaison est de l'ordre de 2% par rapport à l'approche markovienne seule (85.3%

A SVM-based cursive character recognizer

Abstract This paper presents a cursive character recognizer, a crucial module in any cursive word recognition system based on a segmentation and recognition approach. The character classification is achieved by using support vector machines (SVMs) and a neural gas. The neural gas is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not. Once this is done for every letter, the character recognition is performed by SVMs. A database of 57 293 characters was used to train and test the cursive character recognizer. SVMs compare notably better, in terms of recognition rates, with popular neural classifiers, such as learning vector quantization and multi-layer-perceptron. SVM recognition rate is among the highest presented in the literature for cursive character recognition

Normalisation par modèles locaux et reconnaissance par modèles globaux pour la reconnaissance de l'écriture manuscrit

Colloque avec actes et comité de lecture. nationale.National audienceLe principal problème en Reconnaissance de l'Ecriture Manuscrite est la grande variabilité de l'écriture et les distorsions des échantillons. Les modèles élastiques tels que les \hmm\ sont particulièrement efficaces pour absorber ces variations, grâce à l'utilisation d'observations locales et de programmation dynamique. Mais leur vision de la forme reste locale. D'un autre coté, les modèles globaux tels que les Réseaux de Neurones savent faire des corrélations sur la globalité d'un échantillon. Mais leur entrée de taille fixe ne leur permet pas de s'adapter aux variations de longueur, et ils sont très sensibles aux déformations. Pour utiliser les avantages des deux classes de modèles, nous proposons de normaliser les images à l'aide d'un modèle élastique (un \nshp), puis de les analyser à l'aide d'un modèle global (un \svm). Le \nshp\ se focalise sur les caractéristiques importantes en absorbant les distorsions. L'image est normalisée non-linéairement d'après ces informations, et un \svm\ est utilisé pour les corrélations globales et la classification. Les premiers résultats sont encourageants et tendent à confirmer l'intérêt de notre approche