Zone Segmentation and Thinning based Algorithm for Segmentation of Devnagari Text

Character segmentation of handwritten documents is an challenging research topic due to its diverse application environment.OCR can be used for automated processing and handling of forms, old corrupted reports, bank cheques, postal codes and structures. Now Segmentation of a word into characters is one of the major challenge in optical character recognition. This is even more challenging when we segment characters in an offline handwritten document and the next hurdle is presence of broken ,touching and overlapped characters in devnagari script. So, in this paper we have introduced an algorithm that will segment both broken as well as touching characters in devnagari script. Now to segment these characters the algorithm uses both zone segmentation and thinning based techniques. We have used 85 words each for isolated, broken, touching and both broken as well as touching characters individually. Results achieved while segmentation of broken as well as touching are 96.2 % on an average

Research report on Bengla OCR training and testing methods

Includes bibliographical references (page 6-7).In this paper we present the training and recognition mechanism of a Hidden Markov Model (HMM) based multi-font Optical Character Recognition (OCR) system for Bengali character. In our approach, the central idea is to separate the HMM model for each segmented character or word. The system uses HTK toolkit for data preparation, model training and recognition. The Features of each trained character are calculated by applying the Discrete Cosine Transform (DCT) to each pixel value of the character image where the image is divided into several frames according to its size. The extracted features of each frame are used as discrete probability distributions which will be given as input parameters to each HMM model. In the case of recognition, a model for each separated character or word is built up using the same approach. This model is given to the HTK toolkit to perform the recognition using the Viterbi Decoding method. The experimental results show significant performance over models using neural network based training and recognition systems.Md. Abul Hasna

A Study of Techniques and Challenges in Text Recognition Systems

The core system for Natural Language Processing (NLP) and digitalization is Text Recognition. These systems are critical in bridging the gaps in digitization produced by non-editable documents, as well as contributing to finance, health care, machine translation, digital libraries, and a variety of other fields. In addition, as a result of the pandemic, the amount of digital information in the education sector has increased, necessitating the deployment of text recognition systems to deal with it. Text Recognition systems worked on three different categories of text: (a) Machine Printed, (b) Offline Handwritten, and (c) Online Handwritten Texts. The major goal of this research is to examine the process of typewritten text recognition systems. The availability of historical documents and other traditional materials in many types of texts is another major challenge for convergence. Despite the fact that this research examines a variety of languages, the Gurmukhi language receives the most focus. This paper shows an analysis of all prior text recognition algorithms for the Gurmukhi language. In addition, work on degraded texts in various languages is evaluated based on accuracy and F-measure

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field