A review on handwritten character and numeral recognition for Roman, Arabic, Chinese and Indian scripts

Abstract -There are a lot of intensive researches on handwritten character recognition (HCR) for almost past four decades. The research has been done on some of popular scripts such as Roman, Arabic, Chinese and Indian. In this paper we present a review on HCR work on the four popular scripts. We have summarized most of the published paper from 2005 to recent and also analyzed the various methods in creating a robust HCR system. We also added some future direction of research on HCR

DEEP CONVOLUTIONAL NEURAL NETWORK USING A NEW DATASET FOR BERBER LANGUAGE

Currently, Handwritten Character Recognition (HCR) technology has become an interesting and immensely useful technology. It has been explored with highperformance in many languages. However, a few HCR systems are proposed for the Amazigh (Berber) language. Furthermore, the validation of any Amazighhandwritten recognition system remains a major challenge due to no availability of a robust Amazigh database. To address this problem, we first created two new datasets for Tifinagh and Amazigh Latin characters, by extending the well-known EMNIST database with the Amazigh alphabet. And then, we have proposed a handwritten character recognition system, which is based on a deep convolutional neural network to validate the created datasets. The proposed CNN has been trained and tested on our created datasets, and the experimental tests show that it achieves satisfactory results in terms of accuracy and recognition efficiency

Offline Handwritten Kannada Numerals Recognition

Handwritten Character Recognition (HCR) is one of the essential aspect in academic and production fields. The recognition system can be either online or offline. There is a large scope for character recognition on hand written papers. India is a multilingual and multi script country, where eighteen official scripts are accepted and have over hundred regional languages. Recognition of unconstrained hand written Indian scripts is difficult because of the presence of numerals, vowels, consonants, vowel modifiers and compound characters. In this paper, recognition of handwritten Kannada numeral characters is implemented and the different Wavelet features are used as feature extraction in this paper. The zonal densities of different region of an image have been extracted in the database. The database consists of 50 samples of each Kannada numeral character. For classification, the K-Nearest Neighbor method is used. Recognition accuracy of 88% has been achieved

Unicode-driven Deep Learning Handwritten Telugu-to-English Character Recognition and Translation System

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively

Wavelet Coefficients and Gradient Direction for Offline Recognition of Isolated Malayalam Characters

This work attempt to use the wavelet transform coefficients combined with image gradient direction as feature vector for the recognition of isolated handwritten Malayalam characters. It has been established that the number of zero crossings in wavelet transform distinctly characterizes an image. This property has been exploited in this work for the recognition of handwritten characters. The images of 71 characters in Malayalam are considered for the recognition purpose. The segmented image of the symbols are thinned and smoothed for further processing. The feature vector proposed in this work is the combination of number of zero crossings in two level Daubechies (Db4) wavelet transform and gradient direction of the image mapped to twelve regions with each region having 30 degree span. A two level Db4 wavelet transform is applied on each processed symbol and the number of zero crossings in each of 20 sub images are counted and recorded. Gradient direction is combined with this to form the feature vector. Multilayer Perceptron classifier is used for classification. We have obtained an accuracy of 98.8%

PENGENALAN CITRA ANGKA TULISAN TANGAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS

Pengenalan angka tulisan tangan adalah topik yang sering dibahas pada aplikasi-aplikasi Handwritten Character Recognition (HCR). Pada proses pengenalan suatu citra angka tulisan tangan, algoritma K-Nearest Neighbors (KNN) yang menggunakan moment invariant sebagai ekstraksi fitur jarang diaplikasikan. Penelitian ini bertujuan untuk menguji akurasi algoritma K-Nearest Neighbors (KNN) menggunakan ekstraksi fitur moment invariant pada citra angka tulisan tangan yang telah disediakan oleh Modified National Institute of Standards and Technology (MNIST). Simulasi ini mempunyai dua tahapan, yaitu proses ekstraksi citra angka tulisan tangan yang akan menghasilkan tujuh nilai moment invariant dan proses pengenalan citra angka tulisan tangan menggunakan algoritma KNN. Jumlah data yang digunakan adalah 70.000 yang terdiri dari 69.000 data latih dan 1.000 data uji. Nilai Parameter K yang digunakan pada penelitian ini adalah 11. Keakuratan hasil pengenalan berdasarkan simulasi setiap angka dianalisa menggunakan metode confusion matrix. Hasil analisa dengan menggunakan confusion matrix menunjukkan bahwa algoritma K-Nearest Neighbors (KNN) dengan menggunakan ekstraksi fitur moment invariant dapat mengenali angka tulisan tangan dengan akurasi 94,1%. Kata Kunci: KNN, moment invariant, ekstraksi fitur, pengenalan angka, MNIST, confusion matrix

Mobile assisted mathematical equation solver using handwritten character recognition / Mohd Amir Ashraaf Amran

Nowadays, learning Mathematics courses can be very easy and interesting because of technological advances. The traditional ways in solving Mathematical equation have been enhanced from the first invention of abacus then replaced by standard calculator and scientific calculator. Since the beginning of smartphones eras, the technology of scientific calculator has been adapted into smartphones and become as mobile application. Based on the observation, there are several existing applications that used character recognition as scientific calculator. However, most of current applications were lack of effectiveness such as only able to solve simple calculation, difficult to recognize the handwriting document and poor quality of digital document. To overcome the problem, Handwritten Character Recognition (HCR) was used in this project which to enhanced the ability to recognize the handwritten document. The project will be development in native mobile application platform. An Agile methodology has been implied in this application development to ensure all the functionalities meet with the requirement. As the result, this application was under functionality testing to ensure all functionalities are working as required and the output are follow with the requirement. The hopes of this project is to able assist the users especially student to improve solving Mathematical problem in more accurate and faster. For the future work, the proposed application will enlarge the scopes of various kind of Mathematical problem and solutions techniques in order to produces more efficient and effective output

Pengenalan Pola Berbasis OCR untuk Pengambilan Data Bursa Saham

The investor must be able to use instinct to evaluate when to sell and buy stocks. This is, of fact, a weakness for inexperienced investors, in addition to the decision's inaccuracy and the time it takes to evaluate a slew of ineffective results. So that, a support system is needed to help the investors make decisions in buying and selling shares. This support system creates an online analysis curve display through text data in the BEI stock price application. The data processing based on pattern recognition will be carried out so that a buying and selling decision can be made to calculate the profit and loss by investors. As the first step of the whole system, this research has built an image-to-text conversion system based on OCR (Optical Character Recognition) that can convert the non-editable text (.jpg) to be editable (.text) online. After obtaining this .text data, the will used the system in further research to analyze stock buying and selling decisions. According to research on eight companies, the OCR-based image to text conversion has a 96.8% accuracy rate. Meanwhile, using Droid serif, Takao PGhotic, and Waree fonts at 12pt font sizes, it has 100 percent accuracy in Libre Office. Investors in buying and selling shares must analyze when to sell and buy stocks based on instinct. For novice investors, this is a weakness in addition to the inaccuracy of the decision and the time it takes to analyze some ineffective data. This study proposes a solution utilizing OCR (Optical Character Recognition) technology that can convert non-editable text to editable text and allow it to be done online. The application of OCR in this research is to take the text on the IDX stock price data chart so that data processing can be carried out with the principle of recognition patterns so that a buying and selling decision can be calculated by investors predicting stock price fluctuations. The eight companies' testing results for the OCR-based image-to-text conversion obtained 96.8% accuracy. Meanwhile, for testing at the libre office, it has 100% accuracy using the Droid serif, Takao PGhotic, and Waree fonts

Metaheuristic approach on feature extraction and classification algorithm for handwrittten character recognition

Handwritten Character Recognition (HCR) is a process of converting handwritten text into machine readable form and it comprises three stages; preprocessing, feature extraction and classification. This study acknowledged the issues regarding HCR performances particularly at the feature extraction and classification stages. In relation to feature extraction stage, the problem identified is related to continuous and minimum chain code feature extraction at its starting and revisit points due to branches of handwritten character. As for the classification stage, the problems identified are related to the input feature for classification that results in low accuracy of classification and classification model particularly in Artificial Neural Network (ANN) learning problem. Thus, the aim of this study is to extract the continuous chain code feature for handwritten character along with minimising its length and then proceed to develop and enhance the ANN classification model based on the extracted chain code in order to identify the handwritten character better. Four phases were involved in accomplishing the aim of this study. First, thinning algorithm was applied to remove the redundancies of pixel in handwritten character binary image. Second, graph based-metaheuristic feature extraction algorithm was proposed to extract the continuous chain code feature of the handwritten character image while minimising the route length of the chain code. Graph theory was then utilised as a solution representation. Hence, two metaheuristic approaches were adopted; Harmony Search Algorithm (HSA) and Flower Pollination Algorithm (FPA). As a result, HSA graphbased metaheuristic feature extraction algorithm was proposed to extract the continuous chain code feature for handwritten character. Based on the experiment conducted, it was demonstrated that the HSA graph-based metaheuristic feature extraction algorithm showed better performance in generating the shortest route length of chain code with minimum computational time compared to FPA. Furthermore, based on the evaluation of previous works, the proposed algorithm showed notable performance in terms of shortest route length of chain code for extracting handwritten character. Third, a feature vector was derived to address the input feature issue. The derivation of feature vector based on proposed formation rule namely Local Value Formation Rule (LVFR) and Global Value Formation Rule (GVFR) was adopted to create the image features for classification purpose. ANN was applied to classify the handwritten character based on the derived feature vector. Fourth, a hybrid of Firefly Algorithm (FA) and ANN (FA-ANN) classification model was proposed to solve the ANN network learning issue. Confusion Matrix was generated to evaluate the performance of the model in terms of precision, sensitivity, specificity, F-score, accuracy and error rate. As a result, the proposed hybrid FA-ANN classification model is superior in classifying the handwritten characters compared to the proposed feature vector-based ANN with 1.59 percent incremental in terms of accuracy model. Furthermore, the proposed hybrid FA-ANN also exhibits better performances compared to previous related works on HCR