Does color modalities affect handwriting recognition? An empirical study on Persian handwritings using convolutional neural networks

Most of the methods on handwritten recognition in the literature are focused and evaluated on Black and White (BW) image databases. In this paper we try to answer a fundamental question in document recognition. Using Convolutional Neural Networks (CNNs), as eye simulator, we investigate to see whether color modalities of handwritten digits and words affect their recognition accuracy or speed? To the best of our knowledge, so far this question has not been answered due to the lack of handwritten databases that have all three color modalities of handwritings. To answer this question, we selected 13,330 isolated digits and 62,500 words from a novel Persian handwritten database, which have three different color modalities and are unique in term of size and variety. Our selected datasets are divided into training, validation, and testing sets. Afterwards, similar conventional CNN models are trained with the training samples. While the experimental results on the testing set show that CNN on the BW digit and word images has a higher performance compared to the other two color modalities, in general there are no significant differences for network accuracy in different color modalities. Also, comparisons of training times in three color modalities show that recognition of handwritten digits and words in BW images using CNN is much more efficient

Decision Fusion and Contextual Information for Arabic Words Recognition for Computing and Informatics

The study of multiple classifier systems has become recently an area of intensive research in pattern recognition. Also in handwriting recognition, systems combining several classifiers have been investigated. An approach for recognizing the legal amount for handwritten Arabic bank check is described in this article. The solution uses multiple information sources to recognize words. The recognition step is preformed with a parallel combination of three kinds of classifiers using holistic word structural features. The classification stage results are first normalized, and the sum combination is performed as a decision fusion scheme, after which a syntactic analyzer makes final decision on the candidate words. Using this approach, the obtained results are very interesting and promising

Selecting Significant Features for Authorship Invarianceness in Writer Identification

Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain where to find the unique features of individual which also known as Individuality of Handwriting. It relates to invarianceness of authorship where invarianceness between features for intraclass (same writer) is lower than inter-class (different writer). This paper discusses and reports the exploration of significant features for invarianceness of authorship from global shape features by using feature selection technique. The promising results show that the proposed method is worth to receive further exploration in identifying the handwritten authorship

Handwritten Recognition System Based on Machine Learning

مقدمة: يعد التعرف على خط اليد قضية مهمة في الوقت الحاضر ، حيث يمكن أن تكون الكتابة اليدوية صورة أو مستندًا وما إلى ذلك ، تعد قدرة الكمبيوتر على التعرف على الأرقام المكتوبة بخط اليد مهمة جدًا في أكثر من تطبيق مثل تطبيقات الترجمة والقراءة والتعرف على الأرقام. يوفر المشروع المقترح نظامًا يتعرف على الأرقام الإنجليزية المكتوبة بخط اليد ، ويتم تنزيل بيانات الإدخال من مجموعة بيانات عالمية. يتكون النظام المقترح من عدد من المراحل. المرحلة الأولى هي المعالجة المسبقة ، والتي تتضمن تغيير حجم الصور لتكون بحجم واحد (28 * 28) ، ثم يتم تطبيق خطوة (تعيين البيانات). أما بالنسبة لمرحلة التصنيف ، فقد اعتمدت على استخدام خوارزميتين ، خوارزمية KNN والشبكة العصبية (خطأ backpropagation). لبدء عملية تدريب الخوارزميات المختارة ، تم تقسيم البيانات إلى مجموعتين ، مجموعة التدريب ومجموعة الاختبار. تم استخدام خوارزميتين لغرض اختيار أفضلها من خلال تقييم أدائها باستخدام عدد من مقاييس التقييم. تم استخدام الدقة والدقة لغرض تقييم أداء الخوارزميات. كان أداء خوارزمية KNN 0.94 و 0.942 على التوالي عند k = 4. بينما كان أفضل أداء وصلت إليه آلية الشبكة العصبية 0.98673333 و 0.9698 على التوالي ، في العصر = 15. تظهر الشبكة العصبية (خطأ backpropagation) أفضل نتيجة  في مرحلة الاعتراف. طرق العمل: لا تقدم تقنية (KNN) أي افتراضات حول مجموعة البيانات الأساسية. إنه معروف بفعاليته وسهولة استخدامه. إنها خوارزمية تعلم خاضعة للإشراف. لتقدير فئة البيانات غير المسماة ، يتم توفير مجموعة تدريب معنونة تحتوي على نقاط بيانات مقسمة إلى مجموعات عديدة. الاستنتاجات: توضح مؤشرات الدقة والدقة وصفًا دقيقًا لأداء الخوارزميات المستخدمة في النظام المقترح. وصف المؤشرين أداء الخوارزمية (KNN) والتي أعطت النتائج (0.94 و 0.942) على التوالي.Background: Handwriting recognition is an important issue nowadays, where handwriting can be a image, document, etc., the ability of a computer to recognize handwritten numbers is very important in more than one application such as translation, reading and number recognition applications. The proposed project provides a system that recognizes handwritten English numbers, the input data being images downloaded from a global dataset. The proposed system consists of a number of stages. The first stage is the preprocessing, which includes resizing of the images to be one size (28 * 28), and then a step (data mapping) is applied. As for the classification stage, it relied on the use of two algorithms, the KNN algorithm and the neural network (error backpropagation). To start the process of training the selected algorithms, the data was divided into two sets, the training setand the test set. Two algorithms were used for the purpose of choosing the best of them, by evaluating their performance using a number of evaluation metrics. Accuracy and Precision were used for the purpose of evaluating the performance of the algorithms. The performance of the KNN algorithm was 0.94 and 0.942 respectively when k = 4. While the best performance reached by the neural network mechanism was 0.98673333 and 0.9698, respectively, at epoch = 15. The neural network (error backpropagation) is shows the best result in the recognation stage Materials and Methods: K-Nearest Neighbors (KNN) technique makes no assumptions about the basic dataset. It is recognized for its effectiveness and ease of use. It is a supervised learning algorithm. To estimate the category of the unlabeled data, a labeled training set containing data points separated into many groups is supplied. Results: The performance of the KNN model with various values for "K." Since the high value of model accuracy was "0.94", the "4" parameter value is the one that provides the best results and precision was "0.94". Conclusion: The problem of handwritten recognition needs high accuracy and precision indicators show an accurate description of the performance of the algorithms that were employed in the proposed system. The two indicators described the performance of the algorithm (KNN), which gave results (0.94 and 0.942)

Similar exemplar pooling processes underlie the learning of facial identity and handwriting style: Evidence from typical observers and individuals with Autism

Considerable research has addressed whether the cognitive and neural representations recruited by faces are similar to those engaged by other types of visual stimuli. For example, research has examined the extent to which objects of expertise recruit holistic representation and engage the fusiform face area. Little is known, however, about the domain-specificity of the exemplar pooling processes thought to underlie the acquisition of familiarity with particular facial identities. In the present study we sought to compare observers’ ability to learn facial identities and handwriting styles from exposure to multiple exemplars. Crucially, while handwritten words and faces differ considerably in their topographic form, both learning tasks share a common exemplar pooling component. In our first experiment, we find that typical observers’ ability to learn facial identities and handwriting styles from exposure to multiple exemplars correlates closely. In our second experiment, we show that observers with autism spectrum disorder (ASD) are impaired at both learning tasks. Our findings suggest that similar exemplar pooling processes are recruited when learning facial identities and handwriting styles. Models of exemplar pooling originally developed to explain face learning, may therefore offer valuable insights into exemplar pooling across a range of domains, extending beyond faces. Aberrant exemplar pooling, possibly resulting from structural differences in the inferior longitudinal fasciculus, may underlie difficulties recognising familiar faces often experienced by individuals with ASD, and leave observers overly reliant on local details present in particular exemplars

A line-based representation for matching words in historical manuscripts

Cataloged from PDF version of article.In this study, we propose a new method for retrieving and recognizing words in historical documents. We represent word images with a set of line segments. Then we provide a criterion for word matching based on matching the lines. We carry out experiments on a benchmark dataset consisting of manuscripts by George Washington, as well as on Ottoman manuscripts. (C) 2011 Elsevier B.V. All rights reserved

Learning-Based Arabic Word Spotting Using a Hierarchical Classifier

The effective retrieval of information from scanned and written documents is becoming essential with the increasing amounts of digitized documents, and therefore developing efficient means of analyzing and recognizing these documents is of significant interest. Among these methods is word spotting, which has recently become an active research area. Such systems have been implemented for Latin-based and Chinese languages, while few of them have been implemented for Arabic handwriting. The fact that Arabic writing is cursive by nature and unconstrained, with no clear white space between words, makes the processing of Arabic handwritten documents a more challenging problem. In this thesis, the design and implementation of a learning-based Arabic handwritten word spotting system is presented. This incorporates the aspects of text line extraction, handwritten word recognition, partial segmentation of words, word spotting and finally validation of the spotted words. The Arabic text line is more unconstrained than that of other scripts, essentially since it also includes small connected components such as dots and diacritics that are usually located between lines. Thus, a robust method to extract text lines that takes into consideration the challenges in the Arabic handwriting is proposed. The method is evaluated on two Arabic handwritten documents databases, and the results are compared with those of two other methods for text line extraction. The results show that the proposed method is effective, and compares favorably with the other methods. Word spotting is an automatic process to search for words within a document. Applying this process to handwritten Arabic documents is challenging due to the absence of a clear space between handwritten words. To address this problem, an effective learning-based method for Arabic handwritten word spotting is proposed and presented in this thesis. For this process, sub-words or pieces of Arabic words form the basic components of the search process, and a hierarchical classifier is implemented to integrate statistical language models with the segmentation of an Arabic text line into sub-words. The holistic and analytical paradigms (for word recognition and spotting) are studied, and verification models based on combining these two paradigms have been proposed and implemented to refine the outcomes of the analytical classifier that spots words. Finally, a series of evaluation and testing experiments have been conducted to evaluate the effectiveness of the proposed systems, and these show that promising results have been obtained