Issues & Challenges in Urdu OCR

Optical character recognition is a technique that is used to recognized printed and handwritten text into editable text format. There has been a lot of work done through this technology in identifying characters of different languages with variety of scripts. In which Latin scripts with isolated characters (non-cursive) like English are easy to recognize and significant advances have been made in the recognition; whereas, Arabic and its related cursive languages like Urdu have more complicated and intermingled scripts, are not much worked. This paper discusses a detail of various scripts of Urdu language also discuss issues and challenges regarding Urdu OCR. due to its cursive nature which include cursiveness, more characters dots, large set of characters for recognition, more base shape group characters, placement of dots, ambiguity between the characters and ligatures with very slight difference, context sensitive shapes, ligatures, noise, skew and fonts in Urdu OCR. This paper provides a better understanding toward all the possible engendering dilemmas related to Urdu character recognition

Arabic cursive text recognition from natural scene images

© 2019 by the authors. This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years' publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers

ANN-based Innovative Segmentation Method for Handwritten text in Assamese

Artificial Neural Network (ANN) s has widely been used for recognition of optically scanned character, which partially emulates human thinking in the domain of the Artificial Intelligence. But prior to recognition, it is necessary to segment the character from the text to sentences, words etc. Segmentation of words into individual letters has been one of the major problems in handwriting recognition. Despite several successful works all over the work, development of such tools in specific languages is still an ongoing process especially in the Indian context. This work explores the application of ANN as an aid to segmentation of handwritten characters in Assamese- an important language in the North Eastern part of India. The work explores the performance difference obtained in applying an ANN-based dynamic segmentation algorithm compared to projection- based static segmentation. The algorithm involves, first training of an ANN with individual handwritten characters recorded from different individuals. Handwritten sentences are separated out from text using a static segmentation method. From the segmented line, individual characters are separated out by first over segmenting the entire line. Each of the segments thus obtained, next, is fed to the trained ANN. The point of segmentation at which the ANN recognizes a segment or a combination of several segments to be similar to a handwritten character, a segmentation boundary for the character is assumed to exist and segmentation performed. The segmented character is next compared to the best available match and the segmentation boundary confirmed

Automatic Palaeographic Exploration of Genizah Manuscripts

The Cairo Genizah is a collection of hand-written documents containing approximately 350,000 fragments of mainly Jewish texts discovered in the late 19th century. The fragments are today spread out in some 75 libraries and private collections worldwide, but there is an ongoing effort to document and catalogue all extant fragments. Palaeographic information plays a key role in the study of the Genizah collection. Script style, and–more specifically–handwriting, can be used to identify fragments that might originate from the same original work. Such matched fragments, commonly referred to as “joins”, are currently identified manually by experts, and presumably only a small fraction of existing joins have been discovered to date. In this work, we show that automatic handwriting matching functions, obtained from non-specific features using a corpus of writing samples, can perform this task quite reliably. In addition, we explore the problem of grouping various Genizah documents by script style, without being provided any prior information about the relevant styles. The automatically obtained grouping agrees, for the most part, with the palaeographic taxonomy. In cases where the method fails, it is due to apparent similarities between related scripts

Component-based Segmentation of words from handwritten Arabic text

Efficient preprocessing is very essential for automatic recognition of handwritten documents. In this paper, techniques on segmenting words in handwritten Arabic text are presented. Firstly, connected components (ccs) are extracted, and distances among different components are analyzed. The statistical distribution of this distance is then obtained to determine an optimal threshold for words segmentation. Meanwhile, an improved projection based method is also employed for baseline detection. The proposed method has been successfully tested on IFN/ENIT database consisting of 26459 Arabic words handwritten by 411 different writers, and the results were promising and very encouraging in more accurate detection of the baseline and segmentation of words for further recognition