Off-line Handwritten Character Recognition Research Articles

A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN)

Handwritten character recognition falls under the domain of image classification, which has been under research for years. But still, specific gaps need to be highlighted as offline handwritten character recognition (OHCR) with the limitation of the unstructured hierarchy of character classification. However, the idea is to make the machine recognize handwritten human characters. The language focused on in this research paper is English, using offline handwritten character recognition for identifying English characters. There are many publicly available datasets, of which EMNIST is the most challenging. The key idea of this research paper is to recommend a deep learning-based ELBP-CNN method to help recognize English characters. This research paper proposes a deep learning CovNet with feature extraction and novel local binary pattern-based approaches, LBP (AND, OR), that is tested and compared with renowned pre-trained models using transfer learning. These parametric settings address multiple issues and are finalized after experimentation. The same hyperparametric settings were used for all the models under test and E-Character, with the same data augmentation settings. The proposed model, named the E-Character recognizer, produced 87.31% accuracy. It was better than most of the tested pre-trained models and other proposed methods by other researchers. This research paper further highlighted some problems, like misclassification due to the similar structure of characters.

Offline handwritten character recognition with nomograph-based IMVO feature mining with DSRNN-MaxEnt classification

Offline handwritten character recognition with nomograph-based IMVO feature mining with DSRNN-MaxEnt classification

Development of a Benchmark Odia Handwritten Character Database for an Efficient Offline Handwritten Character Recognition with a Chronological Survey

A good benchmark dataset is a primary requirement in the offline handwritten character recognition (HCR) process. Only three handwritten numerals and alphabet datasets from Odia are publicly accessible for study, although many writers have used several datasets in their experiments. In this article, two tasks are done to address this issue. Those are the following: First, an extensive survey focused on various datasets is provided with the methodologies used in chronological order. The second factor is a solution to the lack of publicly available handwritten characters and numeral datasets. A new dataset of handwritten Odia characters with numerals has been developed. Anyone can access this dataset by sending an email to the authors of the article. This dataset was created with the help of 150 volunteers of various age groups, races, and qualifications. Some homogeneous experiments are conducted using deep learning models to evaluate the consistency of the dataset. One heterogeneous trial has also been performed to estimate the complexities of the characters present in the dataset by comparing them with the existing benchmark datasets.

Offline Handwritten Character and Numeral Recognition

Automatic Character Recognition for the handwritten Indic script has listed up as most the challenging area for research in the field of pattern recognition. Although a great amount of research work has been reported, but all the state-of-art methods are limited with optimal features. This article aims to suggest a well-defined recognition model which harnessed upon handwritten Odia characters and numerals by implementing a novel process of decomposition in terms of 3rd level Fast Discrete Curvelet Transform (FDCT) to get higher dimension feature vector. After that, Kernel-Principal Component Analysis (K-PCA) considered to obtained optimal features from FDCT feature. Finally, the classification is performed by using Probabilistic Neural Network (PNN) on handwritten Odia character and numeral dataset from both NIT Rourkela and IIT Bhubaneswar. The outcome of proposed scheme outperforms better as compared to existing model with optimized Gaussian kernel-based feature set.

A Survey on Arabic Handwritten Script Recognition Systems

The optical character recognition (OCR) system is still an active research field in pattern recognition. Such systems can identify, recognize and distinguish electronically between characters and texts, printed or handwritten. They can also do a transformation of such data type into machine-processable form to facilitate the interaction between user and machine in various applications. In this paper, we present the global structure of an OCR system, with its types (on-line and off-line), categories (printed and handwritten) and its main steps. We also focused on off-line handwritten Arabic character recognition and provided a list of the main datasets publicly available. This paper also presents a survey of the works that have been carried out over recent years. Finally, some open issues and potential research directions have been highlighted

A Survey on Arabic Handwritten Script Recognition Systems

The optical character recognition (OCR) system is still an active research field in pattern recognition. Such systems can identify, recognize and distinguish electronically between characters and texts, printed or handwritten. They can also do a transformation of such data type into machine-processable form to facilitate the interaction between user and machine in various applications. In this paper, we present the global structure of an OCR system, with its types (on-line and off-line), categories (printed and handwritten) and its main steps. We also focused on off-line handwritten Arabic character recognition and provided a list of the main datasets publicly available. This paper also presents a survey of the works that have been carried out over recent years. Finally, some open issues and potential research directions have been highlighted

Discrete Cosine Transformation Based Approach for Offline Handwritten Character and Numeral Recognition

Handwritten Character Recognition (HCR) of Indic scripts is one of the most challenging as compared other scripts. Recognition of handwritten character is more difficult one as compares with the printed one due to their similar structure and orientation. To have a proper recognition system for handwritten characters various researchers have adapted various methods and still yet to have more feasibility and high recognition rate to achieve. Here in this proposed work we have completely focused on handwritten characters and numerals of various scripts of different regions of India such as Bengali, Odia scripts. To have a proper solution for the ambiguities that arises in handwritten one and which has been resolved using the Discrete Cosine Transformation (DCT) as feature extraction part. This transformed based featured is used to calculated GLCM values of each images of handwritten numerals and characters. To maintain the novelty of the work in simulation part we have listed up variance nature of the individual’s images. Subsequently we have also harnessed the SVM, Neural Network, and Quadratic Classifier over dataset to report the recognition rate. After validating the proposed work, we can conclude that adopted model is quite helpful in building the automatic recognition system for both handwritten character and numerals to have solution for real time problems.

Recognition of Offline Handwritten Characters using 2D FFT for English and Hindi Scripts

The Handwritten Character Recognition has been a challenging task for the past many decades. This is an old application related to the area of pattern recognition. Handwritten character recognition (HCR) can be classified into two types namely, Online and Offline. As per the literature survey, there are no standard databases for HCR [1] [2] [3] since there are very less number of speakers for any Indian language compared to English. Hence, the database of Indian scripts both for testing and training are to be developed in the laboratory environment. The recognition accuracy for printed / typed characters is more than 99 percent, whereas for the HCR it is around 60 percent. Hence the area of HCR is an open area of research. HCR for Indian languages is at nascent stage compared to English since they contain alphabets and also matra’s / sandhi are complex which make the recognition tougher. The freedom of the scriber in writing the script is also another challenge for achieving the better recognition accuracy. This work describes the handwritten character recognition of both Hindi and English scripts by extracting features using 2D FFT and using the Nearest Neighborhood Classifier. The best recognition accuracy for handwritten character recognition of English and Hindi languages obtained is 70%.

Handwritten Arabic Characters Recognition using a Hybrid Two-Stage Classifier

Handwritten Arabic character recognition presents a big challenge to researchers in the field of pattern recognition. Arabic characters are characterized by their highly-cursive nature and many of them have a similar appearance. For example, the only difference between some of the alphabet characters is the existence of a number dots above or below the main character shape. This paper proposes a system for isolated off-line handwritten Arabic character recognition using the Discrete Cosine Transform (DCT) as the feature extraction method and a two-stage hybrid classifier. The two stages are a Support Vector Machine (SVM) and a neural network (NN). The first stage is a two-class SVM classifier which classifies a character either a character with dot(s) or without dot(s). The output of this stage is used to extend the feature vector of the character by the class value to give it an extra unique feature. The extend feature vector is fed to a multi-class neural network model to classify the character. The proposed approach is tested on a database of Arabic handwritten characters called AlexU Isolated Alphabet (AIA9K) containing 8,737 character images. The experimental results of the first stage classifier showed a high recognition accuracy rate of 99.14%. The proposed two-stage hybrid classifier obtained an average recognition accuracy rate of 91.84% over all Arabic Alphabet characters.

Classification of Handwritten Tamil Characters using Variable Length Puzzle Pieces

Offline handwritten character recognition system has been a challenge for Indian scripts, especially for South Indian languages. Huge number of characters of local languages including alphabets, consonants and composite characters make the recognition system more complicated. A good recognition system for subset of Tamil script, a famous South Indian script, is proposed in this work. Variable length feature vector is extracted from the thinned character image. This extracted feature is given to a novel simple classification algorithm which works based on probability. A subset of Tamil script, 20 character classes, is considered for experiment. The samples were taken from HP Labs dataset for Tamil language and a recognition accuracy of 88.15% has been produced.

Effect of supervised learning methodologies in offline handwritten Thai character recognition

Offline handwritten character recognition is a conversion process of handwriting into machine-encoded text and predominantly used for digitizing handwritten texts and forensic applications. Currently, several techniques and methods are proposed to enhance accuracy of offline handwritten character recognition for many languages spoken across the globe like English, Tamil, Chinese and Arabic. In this paper, a local feature-based approach using supervised learning techniques is proposed to enhance the accuracy of handwritten offline character recognition for Thai alphabets using unsupervised learning for individual character as a class, whereas most of the existing methodologies for Thai character recognition is done with group of similarly looking characters as a class. The classification is operated by using support vector machine (SVM). The accuracy would be the percentage of correct classification for each class. For the result, the highest accuracy is 74.32% which has 144-bit shape features and uniform pattern LBP for the features.

Automated Handwritten Character Recognition using Deep Learning

Computer recognize the human written character, words documents comes under the definition of Handwriting Recognition. It’s been and is still an area under research. It has gained prominence due to numerous applications. Like office automation, historic documents preservation, help for the blind etc. In this paper we provide in detail the existing study in Offline Handwritten Characters Recognition. The techniques used in this area of research. We provide a meticulous literature review on different techniques used in Offline Handwritten Character Recognition (HWCR) in different languages.

Investigation on deep learning for off-line handwritten Arabic character recognition

Handwritten character recognition is a system widely used in the modern world and it is still an important challenge. Traditional machine-learning techniques require careful engineering and considerable domain expertise to transform raw data into a feature vector from which the classifier could classify the input pattern. To cope with this problem, the popular Deep Convolutional Neural Networks (DCNN), introduced recently, have effectively replaced the hand-crafted descriptors with network features and have been shown to provide significantly better results than traditional methods. It is one of the fastest growing areas in machine learning, promising to reshape the future of artificial intelligence. However, the problem with deep learning is that it requires large datasets for training because of the huge number of parameters needed to be tuned by a learning algorithm. CNN model can be used in three different ways: (i) training the CNN from scratch; (ii) using the transfer learning strategy to leverage features from a pre-trained model on a larger dataset; and (iii) keeping the transfer learning strategy and fine-tune the weights of CNN architecture. In this work, we investigate the applicability of DCNN using transfer learning strategies on two datasets; a new expanded version of our recently proposed database for off-line isolated handwritten Arabic character, referred to as OIHACDB and AHCD. Our results showed satisfactory recognition accuracies and outperform all other prominent exiting methods in the field of Handwritten Arabic Character Recognition (HACR).

A Novel Technique for Line Segmentation in Offline Handwritten Gurmukhi Script Documents

Segmentation is an important step in Offline Handwritten Character Recognition (Offline HCR). Line segmentation is an important activity included in segmentation process. Line segmentation is a challenging task and it becomes even more challenging when one needs to segment lines in a skewed offline handwritten document. Improper segmentation decreases the recognition accuracy considerably. In this paper, strip based projection profile technique and smearing technique with contour tracing (proposed combination) have been used for line segmentation in offline handwritten Gurmukhi script documents. We have achieved an accuracy of 98.26% for line segmentation with proposed technique and an accuracy of 93.05% for line segmentation with strip based projection profile technique.

Off-Line Handwritten Character Recognition System Using Support Vector Machine

Selection of classifiers and feature extraction methods has a prime role in achieving best possible classification accuracy in character recognition system. Issues of character recognition system related to choice of classifiers and feature extraction methods can be resolved through these objectives. In this proposed work an efficient Support Vector Machine based off-line handwritten character recognition system has been developed. The experiments have been performed using well known standard database acquired from CEDAR, also seven different approaches of feature extraction techniques have been proposed to construct the final feature vector. It is evident from the experimental results that the performance of Support Vector Machine outperforms other state of art techniques reported in literature.

Handwritten character recognition technology combined with artificial intelligence

The offline handwritten character recognition is an important branch of character recognition, which involves character recognition, image processing, digital signal processing, artificial intelligence, fuzzy mathematics, information theory, computer and other disciplines. In the process of off-line handwritten character recognition, it only processed two-dimensional character dot images, and there are problems such as too many character classes, complex font structure, large deformation of handwritten characters and other issues. Currently off-line handwritten character recognition technology is still immature, which is still in the laboratory research stage. This paper attempted to combine neural network technology and character recognition technology, and came up with effectively new method of handwritten character recognition, finding a feasible new way to solve practical difficulties between handwritten character recognition. In this paper, according to the building process of the multi-level recognition system, the article introduced the preprocessing of character image, character feature extraction, neural networks and optimization algorithms as well as multiple neural networks in turn. In the end, based on experimental results by identification system in Matlab, the author comparatively discussed the performance of the recognition system, analyzing the notices of establishing the identification system.

Offline handwritten pre-segmented character recognition of Gurmukhi script

In this paper, we have proposed a feature extraction technique for recognition of segmented handwritten characters of Gurmukhi script. The experiments have been performed with 7000 specimens of segmented offline handwritten Gurmukhi characters collected from 200 different writers. We have considered the set of 35 basic characters of the Gurmukhi script and have proposed the feature extraction technique based on boundary extents of the character image. PCA based feature selection technique has also been implemented in this work to reduce the dimension of data. We have used k-NN, SVM and MLP classifiers. SVM has been used with four different kernels. In this work, we have achieved maximum recognition accuracy of 93.8% for the 35-class problem when SVM with RBF kernel and 5-fold cross validation technique were employed.

Analysis on Pattern Classification using Artificial Neural Networks

Artificial neural networks (ANN) is one of the most dynamic research and application areas for pattern classification. ANN is the branch of Artificial Intelligence (AI). The network is trained by 'n' number of algorithm like back propagation algorithm. The different combinations of performance functions are used for training the ANN. The back propagation neural network (BPNN) can be used as a highly successful algorithm for pattern classification with suitable combination of performance functions while training and learning ANN. When the maximum likelihood algorithm was compared with back propagation neural network method, the BPNN was more accurate than other algorithms. A Multilayer feed-forward neural network algorithm is also used for pattern classification. However BPNN gives more effective results than other pattern classification algorithms. Handwriting Recognition (or HWR) is the ability of a machine to receive and interpret handwritten input from different sources like paper documents, photographs, touch-screens and other input devices. Various performance functions is examined in this paper so as to get to a conclusion that which function would be better for usage in the network to produce an efficient and effective system. The training of back propagation neural network is done with the application of Offline Handwritten Character Recognition.

Role of Offline Handwritten Character Recognition System in Various Applications

Offline handwritten character recognition system recognizes characters from images. Since the past few decades, many researchers have been developing various handwritten recognition systems for various languages. This paper demonstrates the role and significance of an offline handwritten character recognition system in various applications. General Terms Offline Handwritten Recognition System, Pattern Recognition, Image Processing

A dictionary learning and KPCA-based feature extraction method for off-line handwritten Tibetan character recognition

Kernel principal component analysis (KPCA) is a powerful feature extraction technique. For character recognition, however, the computation cost of KPCA is too high because of much larger sample size of each class. A novel two-stage feature extraction method DL-KPCA that based on dictionary learning and KPCA is proposed for character recognition. In the first stage, with the dictionary learning method K-SVD, a representative sample subset is constructed from the original sample set of each class at first. Then, to the test sample, find its K nearest neighbors from the union of all the constructed sample subsets and consider the classes of their nearest neighbors as the candidate classes. In the second stage, the test sample and the constructed sample subsets of its candidate classes are transformed to the feature space with KPCA, and the test sample is finally classified with K-NN in the feature space. Experimental results on THCDB, a recently developed Tibetan handwritten character sample database, and the reshuffled USPS digit database show that, to character recognition problems, it is feasible to extract the features with the proposed DL-KPCA.