Handwritten Character Recognition of South Indian Scripts: A Review

Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure

Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks

Handwritten character recognition has been the center of research and a benchmark problem in the sector of pattern recognition and artificial intelligence, and it continues to be a challenging research topic. Due to its enormous application many works have been done in this field focusing on different languages. Arabic, being a diversified language has a huge scope of research with potential challenges. A convolutional neural network model for recognizing handwritten numerals in Arabic language is proposed in this paper, where the dataset is subject to various augmentation in order to add robustness needed for deep learning approach. The proposed method is empowered by the presence of dropout regularization to do away with the problem of data overfitting. Moreover, suitable change is introduced in activation function to overcome the problem of vanishing gradient. With these modifications, the proposed system achieves an accuracy of 99.4\% which performs better than every previous work on the dataset.Comment: 5 pages, 6 figures, 3 table

Handwritten Digit Recognition Using Machine Learning Algorithms

Handwritten character recognition is one of the practically important issues in pattern recognition applications. The applications of digit recognition includes in postal mail sorting, bank check processing, form data entry, etc. The heart of the problem lies within the ability to develop an efficient algorithm that can recognize hand written digits and which is submitted by users by the way of a scanner, tablet, and other digital devices. This paper presents an approach to off-line handwritten digit recognition based on different machine learning technique. The main objective of this paper is to ensure effective and reliable approaches for recognition of handwritten digits. Several machines learning algorithm namely, Multilayer Perceptron, Support Vector Machine, NaFDA5; Bayes, Bayes Net, Random Forest, J48 and Random Tree has been used for the recognition of digits using WEKA. The result of this paper shows that highest 90.37% accuracy has been obtained for Multilayer Perceptron

Exploiting Features From Triangle Geometry For Digit Recognition

Triangle is a basic geometry. There are six type of triangle, but scalene triangle was chosen to be used in this research that based on coordinates of corners generated by our proposed algorithm. In this paper, nine features are proposed where six features were derived from coordinates and sides of triangle. Another three features are angle of corners. After features are identified, image will be zoned into 25 zones. The zoning processes are based on Cartesian plan, Vertical and Horizontal zones. From the zoning, from nine features will become 225 features. The features proposed will be used to HODA, MNIST, IFHCDB and BANGLA datasets. Experiments will be conducted using supervised learning that are Support Vector Machine (SVM) and Multi-layer Perceptron (MLP). Results from the experiments will be evaluated with different Cost (c) for the SVM and Learning Rate (LR) for the MLP. Then, the result will be compared to state of the art by other researches

Recognize Arabic Handwritten using CNN Model

احد أكثر التحديات التي تواجه التعلم الآلي هو التعرف على الكتابة بخط اليد ، وخاصة النصوص العربية ، لأن  هناك العديد من أساليب الكتابة للخط العربي. في هذه الورقة ، يُقترح نموذج تحقيق لتمييز النصوص العربية المكتوبة بخط اليد باستخدام الشبكة العصبية التلافيفية (CNN)، مع طبقات متعددة من التطبيع والتنظيم لتقليل وقت التدريب وزيادة الدقة الإجمالية ، تم الوصول الى  دقة تحقق 98 ٪ لمجموعة بيانات Kaggle للغة العربية حيث استخدمت أحرف وأرقام مكتوبة بخط اليد باستخدام Python.One of the most challenges that face machine learning is handwritten recognition, especially Arabic scripts, because many styles found for Arabic font. In this paper, an investigation model is proposed to make recognition for Arabic handwritten scripts utilizing Convolutional Neural Network (CNN), with multi layers of Normalization and Regularization to reduce training time and increase overall accuracy, with validation accuracy 98% for Kaggle dataset for Arabic handwritten characters and digits using Python

An Online Numeral Recognition System Using Improved Structural Features – A Unified Method for Handwritten Arabic and Persian Numerals

With the advances in machine learning techniques, handwritten recognition systems also gained importance. Though digit recognition techniques have been established for online handwritten numerals, an optimized technique that is writer independent is still an open area of research. In this paper, we propose an enhanced unified method for the recognition of handwritten Arabic and Persian numerals using improved structural features. A total of 37 structural based features are extracted and Random Forest classifier is used to classify the numerals based on the extracted features. The results of the proposed approach are compared with other classifiers including Support Vector Machine (SVM), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN). Four different well-known Arabic and Persian databases are used to validate the proposed method. The obtained average 96.15% accuracy in recognition of handwritten digits shows that the proposed method is more efficient and produces better results as compared to other techniques