Principal Component Analysis (PCA) and Modular PCA (MPCA) are well known statistical methods for recognition of facial images. But only PCA/MPCA is found to be insufficient to achieve high classification accuracy required for handwritten character recognition application. This is due to the shortcomings of those methods to represent certain local morphometric information present in the character patterns. On the other hand Quad-tree based hierarchically derived Longest-Run (QTLR) features, a type of popularly used topological features for character recognition, miss some global statistical information of the characters. In this paper, we have introduced a new combination of PCA/MPCA and QTLR features for OCR of handwritten numerals. The performance of the designed feature-combination is evaluated on handwritten numerals of five popular scripts of Indian sub-continent, viz., Arabic, Bangla, Devanagari, Latin and Telugu with Support Vector Machine (SVM) based classifier. From the results it has been observed that MPCA+QTLR feature combination outperforms PCA+QTLR feature combination and most other conventional features available in the literature.

A statistical-topological feature combination for recognition of handwritten numerals

In this paper, we have described the preparation of a benchmark database for research on off-line Optical Character Recognition (OCR) of document images of handwritten Bangla text and Bangla text mixed with English words. This is the first handwritten database in this area, as mentioned above, available as an open source document. As India is a multi-lingual country and has a colonial past, so multi-script document pages are very much common. The database contains 150 handwritten document pages, among which 100 pages are written purely in Bangla script and rests of the 50 pages are written in Bangla text mixed with English words. This database for off-line-handwritten scripts is collected from different data sources. After collecting the document pages, all the documents have been preprocessed and distributed into two groups, i.e., CMATERdb1.1.1, containing document pages written in Bangla script only, and CMATERdb1.2.1, containing document pages written in Bangla text mixed with English words. Finally, we have also provided the useful ground truth images for the line segmentation purpose. To generate the ground truth images, we have first labeled each line in a document page automatically by applying one of our previously developed line extraction techniques [Khandelwal et al., PReMI 2009, pp. 369–374] and then corrected any possible error by using our developed tool GT Gen 1.1. Line extraction accuracies of 90.6 and 92.38% are achieved on the two databases, respectively, using our algorithm. Both the databases along with the ground truth annotations and the ground truth generating tool are available freely at http://code.google.com/p/cmaterdb.

CMATERdb1: a database of unconstrained handwritten Bangla and Bangla–English mixed script document image

Automatic handwriting recognition is an important component for many applications in various fields It is a challenging problem that has received a lot of attention in the past three decades Research has focused on the recognition of Latin languages’ handwriting Fewer studies have been done for the Arabic language In this paper, we present a new dataset of Arabic letters written exclusively by children aged 7–12 which we call Hijja Our dataset contains 47,434 characters written by 591 participants In addition, we propose an automatic handwriting recognition model based on convolutional neural networks (CNN) We train our model on Hijja, as well as the Arabic Handwritten Character Dataset (AHCD) dataset Results show that our model’s performance is promising, achieving accuracies of 97% and 88% on the AHCD dataset and the Hijja dataset, respectively, outperforming other models in the literature

/pdf/arabic-handwriting-recognition-system-using-convolutional-2o12rzg18u.pdf

Arabic handwriting recognition system using convolutional neural network

A methodological study on significance of image processing and its applications in the field of computer vision is carried out here. During an image processing operation the input given is an image and its output is an enhanced high quality image as per the techniques used. Image processing usually referred as digital image processing, but optical and analog image processing also are possible. Our study provides a solid introduction to image processing along with segmentation techniques, computer vision fundamentals and its applied applications that will be of worth to the image processing and computer vision research communities.

A review on image processing and image segmentation

Handwritten character recognition is an active area of research with applications in numerous fields. Past and recent works in this field have concentrated on various languages. Arabic is one language where the scope of research is still widespread, with it being one of the most popular languages in the world and being syntactically different from other major languages. Das et al. \cite{DBLP:journals/corr/abs-1003-1891} has pioneered the research for handwritten digit recognition in Arabic. In this paper, we propose a novel algorithm based on deep learning neural networks using appropriate activation function and regularization layer, which shows significantly improved accuracy compared to the existing Arabic numeral recognition methods. The proposed model gives 97.4 percent accuracy, which is the recorded highest accuracy of the dataset used in the experiment. We also propose a modification of the method described in \cite{DBLP:journals/corr/abs-1003-1891}, where our method scores identical accuracy as that of \cite{DBLP:journals/corr/abs-1003-1891}, with the value of 93.8 percent.

Handwritten Arabic Numeral Recognition using Deep Learning Neural Networks

This paper presents a complete Optical Character Recognition (OCR) system for camera captured image/graphics embedded textual documents for handheld devices. At first, text regions are extracted and skew corrected. Then, these regions are binarized and segmented into lines and characters. Characters are passed into the recognition module. Experimenting with a set of 100 business card images, captured by cell phone camera, we have achieved a maximum recognition accuracy of 92.74%. Compared to Tesseract, an open source desktop-based powerful OCR engine, present recognition accuracy is worth contributing. Moreover, the developed technique is computationally efficient and consumes low memory so as to be applicable on handheld devices.

/pdf/design-of-an-optical-character-recognition-system-for-camera-560sou34a7.pdf

Design of an Optical Character Recognition System for Camera- based Handheld Devices

Handwritten numeral recognition is in general a benchmark problem of Pattern Recognition and Artificial Intelligence Compared to the problem of printed numeral recognition, the problem of handwritten numeral recognition is compounded due to variations in shapes and sizes of handwritten characters Considering all these, the problem of handwritten numeral recognition is addressed under the present work in respect to handwritten Arabic numerals Arabic is spoken throughout the Arab World and the fifth most popular language in the world slightly before Portuguese and Bengali For the present work, we have developed a feature set of 88 features is designed to represent samples of handwritten Arabic numerals for this work It includes 72 shadow and 16 octant features A Multi Layer Perceptron (MLP) based classifier is used here for recognition handwritten Arabic digits represented with the said feature set On experimentation with a database of 3000 samples, the technique yields an average recognition rate of 9493% evaluated after three-fold cross validation of results It is useful for applications related to OCR of handwritten Arabic Digit and can also be extended to include OCR of handwritten characters of Arabic alphabet

Handwritten Arabic Numeral Recognition using a Multi Layer Perceptron

Multi-lingual scene text detection and language identification

Separation of the text regions from background texture and graphics is an important step of any optical character recognition sytem for the images containg both texts and graphics. In this paper, we have presented a novel text/graphics separation technique for business card images captured with a cell-phone camera. At first, the background is eliminated at a coarse level based on intensity variance. This makes the foreground components distinct from each other. Then the non-text components are removed using various characteristic features of text and graphics. Finally, the text regions are skew corrected and binarized for further processing. Experimenting with business card images of various resolutions, we have found an optimum performance of 98.54% with 0.75 MP images, that takes 0.17 seconds processing time and 1.1 MB peak memory on a moderately powerful computer (DualCore 1.73 GHz Processor, 1 GB RAM, 1 MB L2 Cache). The developed technique is computationally efficient and consumes low memory so as to be applicable on mobile devices.

Text/Graphics Separation for Business Card Images for Mobile Devices

Numeral recognition is an important preliminary step for optical character recognition, document understanding and others. Deep learning based numeral recognition models have achieved huge popularity among the researchers around the world since last few years. Several convolutional neural network (CNN) based architectures have been proposed and many of those have achieved state-of-the-art results in numeral recognition. In this paper, we have explored CNN based architectures for handwritten Arabic numeral recognition. We have also developed a handwritten Arabic numerals dataset using various morphological operations on an existing dataset thus increasing the size of the dataset from 3000 to 72,000 images. A modification of previously proposed CNN architecture has given us an accuracy of 98.91% and our proposed architecture has produced 99.76%, which is comparable to state-of-the-art results found in the domain of handwritten Arabic numeral recognition.

Ayatullah Faruk Mollah

Papers

Design of an Optical Character Recognition System for Camera- based Handheld Devices

Handwritten Arabic Numeral Recognition using a Multi Layer Perceptron

Multi-lingual scene text detection and language identification

Text/Graphics Separation for Business Card Images for Mobile Devices

Handwritten Arabic numerals recognition using convolutional neural network