An Innovative Approach for Automatic Character Recognition of Indian Languages

Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts. Objective: This article presents a novel approach for online handwritten numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil. Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters. Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard. Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.

An internet of things (IoT) is an exclusive method, were its impact on the enactments of human life is very trendy. This research on library control system operates on the basis of IoT and optical character recognition (OCR) algorithm rules and its training. A closed-circuit television (CCTV) watched mechanism is created to control the book issuing and returning phenomenon via tag studying system in the library. In this proposed work text file is converted into an audio file. This audio file is being played and the contents of the book can be heard via the headset. This unique function of the OCR helps blind people. Now a days OCR widely focused in machine processes such as machine transformation, text to speech extraction and text data mining. It utilized in various area of research in artificial intelligence, computer vision and pattern recognition. Using OCR to scan the damaged book in the library converted into pdf format the book gets new life and sharing the contents to multiple readers. In this paper aims to implement IoT based library management system to maintaining books in digital format.

Ahstract-This study provides a novel brain-computer interface (BCI) approach for character recognition. The character recognition task is a two class classification problem. The key objective of the character recognition is to detect the P300 signal from the set of electroencephalogram (EEG) signals. The character is predicted from the detected P300 signal and row/column information of the oddball paradigm. The signal-to-noise ratio (SNR) of the electroencephalogram (EEG) signal is low. Ensemble of classifier is used to reduce the classifier variabilIty and enhance the SNR of the acquired signal. Here ensemble of SVMs (ESVMs) is used as classifier. The distribution of dataset is imbalanced due to its paradigm. A novel approach is applied in this work to balance the dataset. The proposed algorithm is evaluated on dataset IIb of BCI Competition II and dataset II of the BCI Competition III. The experimental results show that the proposed method outperforms the state-of-the-art character recognition performance.

Character Recognition is a prominent field of research in pattern recognition. Low error rate of methods presented in other papers indicates that the problem of recognizing typewritten fonts is solved, using mainly deep learning methods. However, those algorithms do not work as well for recognizing handwritten characters, since learning discriminative features is much more complex for this problem so it still remains an interesting issue from research point of view. This document presents a proposal to solve handwritten characters recognition problem using k3m skeletonization algorithm. The idea has been designed to work correctly regardless of the width of the characters, their thickness or shape. This is an innovative method not considered in previous papers, which yields results comparable to the best ones achieved so far, what is proven in tests. The method can be also easily extended to signs other than glyphs in Latin alphabet.

With the evolving technological era, the optical character recognition systems have substantial execution, considering the widespread use of daily hand-written human transaction. Optical Character Recognition (OCR) is an implementation of Computer Vision that digitizes numerous hand dealt documents for further analysis and formatting. OCR is achieved by various ways such as discriminative analysis and deep learning. This paper focuses on evaluating deep learning models on a hand-written compiled dataset of Urdu digits. The evaluation is performed for deep convolutional learning algorithms; VGGNet16, InceptionV3, ResNet50, and DenseNet121. The convolutional models are pre-trained on the ImageNet. The model weights of fully connected layers have been evaluated, reducing the training time of the convolutional layers. The testing accuracy for the compiled dataset is observed as, ResNet50 with 96%, InceptionV3 with 95%, VGGNet16 with 95% and DenseNet121 with 94%.

The handwritten character recognition (HCR) has grown significance in document digitisation, multilingual text processing, and automated form interpretation. Among the different Indic scripts it is relatively underexplored, despite a large population of speakers. This paper provides a comprehensive review of the state of the art of Artificial Intelligence (AI) models for the detection and recognition of characters from Gujarati handwritten text. We critically review over 30 work references focusing on the benefits and limitations of traditional machine learning (ML) methods, hybrid models and state-of-the-art deep learning (DL) architectures. We study the unique features of the Gujarati Script including diacritical marks and visually similar subset of characters and their impact on identification quotient. In addition, we discuss important challenges such as the lack of large-scale public datasets, variations in handwriting styles, and the details of Gujarati ligatures. By synthesising current knowledge, identifying methodological gaps, and providing potential future directions, this paper aims to assist both novices and experienced researchers in designing robust, efficient, and scalable solutions for Gujarati HCR.

Automatic License Plate Recognition (ALPR) is a very widely used system in applications such as parking management, theft detection, traffic control and management etc. Most of the existing ALPR systems fail to showcase acceptable performance on real time images/video scenes. This work proposes and demonstrates implementation of a deep learning-based approach to locate license plates of four wheeler vehicles thereby enabling optical character recognition (OCR) to recognize the characters and numbers on the located plates in real time. The proposed system is decomposed into three sub-blocks viz. Vehicle image/video acquisition, License plate localization and OCR. A simple setup using a reasonable resolution webcam has been designed to capture images/videos of vehicles at some entry point. We propose to utilize Single Shot Detector (SSD) based Mobilenet V1 architecture to localize the license plates. The hyper parameters of this architecture are selected with rigorous experimentation so as to avoid over-fitting. We have compared performance of two OCRs viz. Tesseract OCR, Easy OCR and found the superiority of Easy OCR since it utilizes deep learning approach for character recognition. NVIDIA Jetson Nano and Raspberry Pi 3B hardware platforms have been used to implement the entire system. The parameters of these three sub-blocks have been optimized to yield real time performance of ALPR with acceptable accuracy. The proposed and implemented system on Jetson Nano allows processing of videos for ALPR having accuracy more than 95%.

Character recognition plays an important role in the modern world. It makes human's job easier when solving more complex problems. We have developed and implemented a rotation invariant character recognition technique by extending the eigenface method previously presented in the literature. Our proposed approach treats each character as a two-dimensional recognition problem, taking advantage of the fact that characters can be described by a small set of 2D characteristic views of different angles (0deg-360deg). Character images of different angles are projected onto a feature space (eigen space) that best encodes the variation among known character images. The proposed system is based on principal component analysis which makes it efficient in learning and recognizing characters of different angle.

The problem of Optical Character Recognition is as follows- here input is a scanned image or a printed or handwritten text and the output is a computer readable version of the input content. A lot of research work has been done on OCR worldwide for different languages. Though ‘Bengali’ is the second most popular script and language in Indian subcontinent and the fifth most popular language in the world, a fewer no. of research publications are there. The application areas of character recognition are increasing remarkably. Here we have proposed a new approach of handwritten Bengali character recognition using Graph Based Chain Code Method. Our aim is to achieve maximum recognition rate with minimum processing time.

The quality of character image plays an important role for the performance of character recognition system. However there is no good way to measure the recognition difficulty of a given character image. For the given character image with unknown quality, it is improper that apply the single character database to recognize it by the same feature and the same classifier. This paper proposed a novel approach for multi-resolution character recognition whose feature is extracted directly from gray-scale image and classification is adaptive classification which adaptively selects the appropriate character database and classifiers by evaluating the image quality of the input character. A resolution evaluation algorithm based on gray distribution feature was proposed to decide the adaptive classification weights for the classifiers, which make the classification have the higher probability of being the correct decision. Experiment results demonstrate the proposed approach highly improved the performance of character recognition system.

Offline Script Identification from multilingual Indic-script documents: A state-of-the-art

Online handwriting recognition refers to the problem of machine recognition of handwriting captured in the form of pen trajectories. The recognition technology holds significant promise for Indic scripts, given that the Indic languages are used by a sixth of the world’s population, and the greater ease of use of handwriting-based text input for these scripts compared to keyboard-based methods. Even though the recognition of handwritten Devanagari, Bangla, and Tamil has received significant attention in recent times, one may say that research efforts directed at Indic script recognition in general are in their early stages. The structure of the scripts and the variety of shapes and writing styles pose challenges that are different from other scripts and hence require customized techniques for feature representation and recognition. In this chapter, we describe the challenges in recognizing online handwriting in Indic scripts and provide an overview of the state of the art for isolated character and word recognition. We then present in brief some of the promising applications, starting with handwriting-based text input systems (IMEs) that have been built for entering Indic scripts. In the last section, we provide a few pointers to resources such as tools and data sets that are currently available for online Indic script recognition research. endabstract

The article provides a research in pattern recognition using a hardware implemented classifier based on morphological associative memory (MAM). Most neural network (NN) models used for this purpose have a complex learning rule. Instead, we propose a morphological classifier that has a simple learning rule based on mathematical morphology and presents an infinity storage capacity. Initially, we define a standard for the image as pre-requirement for pre-processing. The pre-processing stage is performed to reduce the dimensionality of MAM's data input in order to act as an interface between the inputs and the classifier. A practical application on recognizing characters from the Roman alphabet shows the versatility of this architecture.

Zoning features are of the most popular and efficient statistical features that provide high speed and low complexity for character and word recognition. They are calculated by the density of pixels or pattern characteristics in several zones we divide the pattern frame. In this paper, we introduce the idea of adaptive zoning features that are extracted after adjusting the position of every zone based on local pattern information. This adjustment is performed by moving every zone towards the pattern body. This process is based on the maximization of the local pixel density around each zone. We have extensively tested our approach for character and word recognition as well as for using the pixel density or pattern characteristics in every zone. For all cases, we have recorded a significant improvement when the zoning features are used in the proposed adaptive way.

This paper presents an approach for grouping and recognition of handwritten characters. The approach uses an efficient feature called character intensity code (CIC). A hierarchical recognition methodology based on the structural details of the characters is adopted. At the first level, similar structured characters are grouped together, and the second level is used for individual character recognition. Support vector machine is used for classification which achieves an overall accuracy of 93.61 %.