Optical character recognition

About: Optical character recognition is a research topic. Over the lifetime, 7342 publications have been published within this topic receiving 158193 citations. The topic is also known as: OCR & optical character reader.

Mobile visual search on printed documents using text and low bit-rate features

Abstract: We present a novel mobile printed document retrieval system that utilizes both text and low bit-rate features. On the client phone, text are detected using an algorithm based on edge-enhanced Maximally Stable Extremal Regions. The title text image patch is rectified using a gradient based algorithm and recognized using Optical Character Recognition. Low bit-rate image features are extracted from the query image. Both text and compressed features are sent to a server. On the server, the title text is used for on-line search and the features are used for image-based comparison. The proposed system is capable of web-scale document retrieval using title text without the need of constructing a document image database. Using features for image-based comparison, we can reliably match retrieved documents to the query document. Last, by using text and low bit-rate features, we can reduce the transmitted query size significantly.

Using SMT for OCR error correction of historical texts

Abstract: A trend to digitize historical paper-based archives has emerged in recent years, with the advent of digital optical scanners. A lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into electronic versions that can be manipulated by a computer. For this purpose, Optical Character Recognition (OCR) systems have been developed to transform scanned digital text into editable computer text. However, different kinds of errors in the OCR system output text can be found, but Automatic Error Correction tools can help in performing the quality of electronic texts by cleaning and removing noises. In this paper, we perform a qualitative and quantitative comparison of several error-correction techniques for historical French documents. Experimentation shows that our Machine Translation for Error Correction method is superior to other Language Modelling correction techniques, with nearly 13% relative improvement compared to the initial baseline.

Improved Recognition Results of Medieval Handwritten Gurmukhi Manuscripts Using Boosting and Bagging Methodologies

Abstract: Recognition of medieval handwritten Gurmukhi manuscripts is an essential process for resourceful contents exploitation of the priceless information contained in them. There are numerous Gurmukhi script ancient manuscripts from fifteenth to twentieth century’s. In this paper, we have considered, work written by various persons from 18th to 20th centuries. For recognition, we have used various feature extraction techniques like zoning, discrete cosine transformations, and gradient features and different combinations of these features. For classification, four classifiers, namely, k-NN, SVM, Decision Tree, Random Forest individual and combinations of these four classifiers with voting scheme have been considered. Adaptive boosting and bagging have been explored for improving the recognition results and achieves the new state of the art for recognition of medieval handwritten Gurmukhi manuscripts recognition. Using this proposed framework, maximum recognition accuracy of 95.91% has been achieved using adaptive boosting technique and a combination of four different classifiers considered in this paper. To the best of our knowledge, this work is the successful attempt towards recognition of medieval handwritten Gurmukhi manuscripts and it can lead towards the development of optical character recognition systems for recognizing medieval handwritten documents in other Indic and non-Indic scripts as well.

AON: Towards Arbitrarily-Oriented Text Recognition

Abstract: Recognizing text from natural images is a hot research topic in computer vision due to its various applications. Despite the enduring research of several decades on optical character recognition (OCR), recognizing texts from natural images is still a challenging task. This is because scene texts are often in irregular (e.g. curved, arbitrarily-oriented or seriously distorted) arrangements, which have not yet been well addressed in the literature. Existing methods on text recognition mainly work with regular (horizontal and frontal) texts and cannot be trivially generalized to handle irregular texts. In this paper, we develop the arbitrary orientation network (AON) to directly capture the deep features of irregular texts, which are combined into an attention-based decoder to generate character sequence. The whole network can be trained end-to-end by using only images and word-level annotations. Extensive experiments on various benchmarks, including the CUTE80, SVT-Perspective, IIIT5k, SVT and ICDAR datasets, show that the proposed AON-based method achieves the-state-of-the-art performance in irregular datasets, and is comparable to major existing methods in regular datasets.

Classification of document page images based on visual similarity of layout structures

Abstract: Searching for documents by their type or genre is a natural way to enhance the effectiveness of document retrieval. The layout of a document contains a significant amount of information that can be used to classify a document's type in the absence of domain specific models. A document type or genre can be defined by the user based primarily on layout structure. Our classification approach is based on 'visual similarity' of the layout structure by building a supervised classifier, given examples of the class. We use image features, such as the percentages of tex and non-text (graphics, image, table, and ruling) content regions, column structures, variations in the point size of fonts, the density of content area, and various statistics on features of connected components which can be derived from class samples without class knowledge. In order to obtain class labels for training samples, we conducted a user relevance test where subjects ranked UW-I document images with respect to the 12 representative images. We implemented our classification scheme using the OC1, a decision tree classifier, and report our findings.