A Complete Workflow for Development of Bangla OCR

Developing a Bangla OCR requires bunch of algorithm and methods. There were many effort went on for developing a Bangla OCR. But all of them failed to provide an error free Bangla OCR. Each of them has some lacking. We discussed about the problem scope of currently existing Bangla OCR's. In this paper, we present the basic steps required for developing a Bangla OCR and a complete workflow for development of a Bangla OCR with mentioning all the possible algorithms required

A Survey on Optical Character Recognition System

Optical Character Recognition (OCR) has been a topic of interest for many years. It is defined as the process of digitizing a document image into its constituent characters. Despite decades of intense research, developing OCR with capabilities comparable to that of human still remains an open challenge. Due to this challenging nature, researchers from industry and academic circles have directed their attentions towards Optical Character Recognition. Over the last few years, the number of academic laboratories and companies involved in research on Character Recognition has increased dramatically. This research aims at summarizing the research so far done in the field of OCR. It provides an overview of different aspects of OCR and discusses corresponding proposals aimed at resolving issues of OCR

Experimental Test of the validity of "Isotropic" Approximation for the Mechanical Behaviour of Clay

Experimental data from axially symmetric compression test at constant mean pressure p on kaolinite clay are used to study the validity of an "isotropic" modelling as a function of the overconsolidation ratio (OCR).The isotropic assumption is found to be quite good for 2<OCR<3 and/or in the range of small deformation for OCR>4. For very large OCR (OCR >10), anisotropic response is observed at few percents of axial deformation. Relation with anisotropic distribution of local forces is made. Pacs # : 5.40 ; 45.70 ; 62.20 ; 83.70.FnComment: 6 pages + 1 page, 1 figur

K-Algorithm A Modified Technique for Noise Removal in Handwritten Documents

OCR has been an active research area since last few decades. OCR performs the recognition of the text in the scanned document image and converts it into editable form. The OCR process can have several stages like pre-processing, segmentation, recognition and post processing. The pre-processing stage is a crucial stage for the success of OCR, which mainly deals with noise removal. In the present paper, a modified technique for noise removal named as K-Algorithm has been proposed, which has two stages as filtering and binarization. The proposed technique shows improvised results in comparison to median filtering technique

OCR Post-Processing Error Correction Algorithm using Google Online Spelling Suggestion

With the advent of digital optical scanners, a lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into an electronic version that can be manipulated by a computer. For this purpose, OCR, short for Optical Character Recognition was developed to translate scanned graphical text into editable computer text. Unfortunately, OCR is still imperfect as it occasionally mis-recognizes letters and falsely identifies scanned text, leading to misspellings and linguistics errors in the OCR output text. This paper proposes a post-processing context-based error correction algorithm for detecting and correcting OCR non-word and real-word errors. The proposed algorithm is based on Google's online spelling suggestion which harnesses an internal database containing a huge collection of terms and word sequences gathered from all over the web, convenient to suggest possible replacements for words that have been misspelled during the OCR process. Experiments carried out revealed a significant improvement in OCR error correction rate. Future research can improve upon the proposed algorithm so much so that it can be parallelized and executed over multiprocessing platforms.Comment: LACSC - Lebanese Association for Computational Sciences, http://www.lacsc.org/; Journal of Emerging Trends in Computing and Information Sciences, Vol. 3, No. 1, January 201

Changing Opinions in a Changing World: a New Perspective in Sociophysics

We propose a new model of opinion formation, the Opinion Changing Rate (OCR) model. Instead of investigating the conditions that allow consensus in a world of agents with different opinions, we study under which conditions a group of agents with a different natural tendency (rate) to change opinion can find agreement. The OCR is a modified version of the Kuramoto model, one of the simplest models for synchronization in biological systems, here adapted to a social context. By means of several numerical simulations we illustrate the richness of the OCR model dynamics and its social implications.Comment: 18 pages, 11 figures, to appear in Int. J. Mod. Phys. C 16, issue

Optical Character Recognition (OCR) for Telugu: Database, Algorithm and Application

Telugu is a Dravidian language spoken by more than 80 million people worldwide. The optical character recognition (OCR) of the Telugu script has wide ranging applications including education, health-care, administration etc. The beautiful Telugu script however is very different from Germanic scripts like English and German. This makes the use of transfer learning of Germanic OCR solutions to Telugu a non-trivial task. To address the challenge of OCR for Telugu, we make three contributions in this work: (i) a database of Telugu characters, (ii) a deep learning based OCR algorithm, and (iii) a client server solution for the online deployment of the algorithm. For the benefit of the Telugu people and the research community, we will make our code freely available at https://gayamtrishal.github.io/OCR_Telugu.github.io/Comment: Accepted to IEEE International Conference on Image Processing 201

Upcycle Your OCR: Reusing OCRs for Post-OCR Text Correction in Romanised Sanskrit

We propose a post-OCR text correction approach for digitising texts in Romanised Sanskrit. Owing to the lack of resources our approach uses OCR models trained for other languages written in Roman. Currently, there exists no dataset available for Romanised Sanskrit OCR. So, we bootstrap a dataset of 430 images, scanned in two different settings and their corresponding ground truth. For training, we synthetically generate training images for both the settings. We find that the use of copying mechanism (Gu et al., 2016) yields a percentage increase of 7.69 in Character Recognition Rate (CRR) than the current state of the art model in solving monotone sequence-to-sequence tasks (Schnober et al., 2016). We find that our system is robust in combating OCR-prone errors, as it obtains a CRR of 87.01% from an OCR output with CRR of 35.76% for one of the dataset settings. A human judgment survey performed on the models shows that our proposed model results in predictions which are faster to comprehend and faster to improve for a human than the other systems.Comment: This paper has been accepted as a full paper in the SIGNLL Conference on Computational Natural Language Learning (CoNLL), 2018. The code, data and the supplementary material is available at https://github.com/majumderb/sanskrit-oc

OCRAPOSE II: An OCR-based indoor positioning system using mobile phone images

In this paper, we propose an OCR (optical character recognition)-based localization system called OCRAPOSE II, which is applicable in a number of indoor scenarios including office buildings, parkings, airports, grocery stores, etc. In these scenarios, characters (i.e. texts or numbers) can be used as suitable distinctive landmarks for localization. The proposed system takes advantage of OCR to read these characters in the query still images and provides a rough location estimate using a floor plan. Then, it finds depth and angle-of-view of the query using the information provided by the OCR engine in order to refine the location estimate. We derive novel formulas for the query angle-of-view and depth estimation using image line segments and the OCR box information. We demonstrate the applicability and effectiveness of the proposed system through experiments in indoor scenarios. It is shown that our system demonstrates better performance compared to the state-of-the-art benchmarks in terms of location recognition rate and average localization error specially under sparse database condition.Comment: 14 pages, 22 Figure

OCR extensions - local identifiers, labeled GUIDs, file IO, and data block partitioning

We present several proposals for extending the Open Community Runtime (OCR) specification. The extension are identifiers with local validity, which use the concept of futures to provide OCR implementations more optimization opportunities, labeled GUIDs with creator functions, which are based on the local identifiers and allow the developer to create arrays of OCR objects that are safe from race conditions in case of concurrent creation of objects, a simple file IO interface, which builds on top of the existing data block concepts, and finally data block partitioning, which allows better control and flexibility in situations where multiple tasks want to access disjoint parts of a data block