Mohammad Tanvir Parvez

Offline arabic handwritten text recognition: A Survey

Research in offline Arabic handwriting recognition has increased considerably in the past few years. This is evident from the numerous research results published recently in major journals and conferences in the area of handwriting recognition. Features and classifications techniques utilized in recent research work have diversified noticeably compared to the past. Moreover, more efforts have been diverted, in last few years, to construct different databases for Arabic handwriting recognition. This article provides a comprehensive survey of recent developments in Arabic handwriting recognition. The article starts with a summary of the characteristics of Arabic text, followed by a general model for an Arabic text recognition system. Then the used databases for Arabic text recognition are discussed. Research works on preprocessing phase, like text representation, baseline detection, line, word, character, and subcharacter segmentation algorithms, are presented. Different feature extraction techniques used in Arabic handwriting recognition are identified and discussed. Different classification approaches, like HMM, ANN, SVM, k-NN, syntactical methods, etc., are discussed in the context of Arabic handwriting recognition. Works on Arabic lexicon construction and spell checking are presented in the postprocessing phase. Several summary tables of published research work are provided for used Arabic text databases and reported results on Arabic character, word, numerals, and text recognition. These tables summarize the features, classifiers, data, and reported recognition accuracy for each technique. Finally, we discuss some future research directions in Arabic handwriting recognition.

KHATT: An open Arabic offline handwritten text database

Abstract A comprehensive Arabic handwritten text database is an essential resource for Arabic handwritten text recognition research. This is especially true due to the lack of such database for Arabic handwritten text. In this paper, we report our comprehensive Arabic offline Handwritten Text database (KHATT) consisting of 1000 handwritten forms written by 1000 distinct writers from different countries. The forms were scanned at 200, 300, and 600 dpi resolutions. The database contains 2000 randomly selected paragraphs from 46 sources, 2000 minimal text paragraph covering all the shapes of Arabic characters, and optionally written paragraphs on open subjects. The 2000 random text paragraphs consist of 9327 lines. The database forms were randomly divided into 70%, 15%, and 15% sets for training, testing, and verification, respectively. This enables researchers to use the database and compare their results. A formal verification procedure is implemented to align the handwritten text with its ground truth at the form, paragraph and line levels. The verified ground truth database contains meta-data describing the written text at the page, paragraph, and line levels in text and XML formats. Tools to extract paragraphs from pages and segment paragraphs into lines are developed. In addition we are presenting our experimental results on the database using two classifiers, viz. Hidden Markov Models (HMM) and our novel syntactic classifier. The database is made freely available to researchers world-wide for research in various handwritten-related problems such as text recognition, writer identification and verification, forms analysis, pre-processing, segmentation. Several international research groups/researchers acquired the database for use in their research so far.

KHATT: Arabic Offline Handwritten Text Database

In this paper, we report our comprehensive Arabic offline Handwritten Text database (KHATT) after completion of the collection of 1000 handwritten forms written by 1000 writers from different countries. It is composed of an image database containing images of the written text at 200, 300, and 600 dpi resolutions, a manually verified ground truth database that contains meta-data describing the written text at the page, paragraph, and line levels. A formal verification procedure is implemented to align the handwritten text with its ground truth at the form, paragraph and line levels. Tools to extract paragraphs from pages and segment paragraphs into lines are developed. Preliminary experiments on Arabic handwritten text recognition are conducted using sample data from the database and the results are reported. The database will be made freely available to researchers world-wide for research in various handwritten-related problems such as text recognition, writer identification and verification, etc.

Handwriting synthesis: classifications and techniques

Handwriting synthesis is the automatic generation of data that resemble natural handwriting. Although handwriting synthesis has recently gained increasing interest, the area still lacks a stand-alone review. This paper provides classifications for the different aspects of handwriting synthesis. It presents the applications, techniques, and evaluation methods for handwriting synthesis based on the several aspects that we identify. Then, it discusses various synthesis techniques. To the best of our knowledge, this paper is the only stand-alone survey on this topic, and we believe it can serve as a useful reference for the researchers in the field of handwriting synthesis.

ICFHR2014 Competition on Arabic Writer Identification Using AHTID/MW and KHATT Databases

This paper describes the first edition of the Arabic writer identification competition using AHTID/MW and KHATT databases held in the context of the 14th International Conference on Frontiers in Handwriting Recognition (ICFHR2014). This competition has used the new freely available Arabic Handwritten Text Images Database written by Multiple Writers (AHTID/MW) and the Arabic handwritten text database called KHATT presented in ICFHR2012. We propose three tasks in this Arabic writer identification competition: the first and second are based respectively on word and text line level using the AHTID/MW database and the third one is paragraph based using the KHATT database. We received one system for the second task, three systems for the third task and none for the first task. All systems are tested in a blind manner using a set of images kept internal. A short description of the participating groups, their systems, the experimental setup, and the observed results are presented.

Secure Arabic Handwritten CAPTCHA Generation Using OCR Operations

Handwritten CAPTCHAs can be generated from pre-written or synthesized words, with added distortions and noise to survive OCR attacks. This paper takes a different approach for generating CAPTCHAs: use OCR operations themselves to secure the CAPTCHAs. Therefore, we utilize a number of operations found in many handwriting recognition systems (like, segmentation, baseline detection, etc.) to distort a pre-written word image itself, so that breaking the resulting CAPTCHA becomes more difficult. These OCR operations are in addition to the global image distortions that are generally done on the CAPTCHAs. The proposed method is reported for Arabic handwritten words as the cursive script of Arabic allows various OCR operations on it. To the best of our knowledge, this work is the first to generate Arabic handwritten CAPTCHAs. We evaluate our method on KHATT database of offline Arabic handwritten text. In terms of usability, we have achieved 88% to 90% accuracy. Security evaluation is done using holistic word recognition with accuracy less than 0.5%. Lexicon based attack is made difficult by working at Arabic sub-word level and then randomly selecting sub-words to build a CAPTCHA.

Optimised cubic spline approximations of image contours using points suppression

In this study, the author presents an algorithm for approximating the contour of a digital planar image by cubic splines. In the authors’ method, a subset of points (called corners) from the contour is selected. These corners are used to segment the contour and each segment is then approximated by a cubic spline. Parameters of the fitted splines are estimated by optimisation methods. The novelty of the proposed approach lies in the way the corners are selected. An initial set of corners are first selected using a process which is called as iterative points-suppression. This initial set is further reduced by a novel technique termed spline-suppression. The result is a very compact cubic spline representation of the contour using few corners on the contour. The effectiveness of the proposed method is demonstrated on two large databases: MPEG7_CE-Shape-1_Part_B database and a database of handwritten characters.

Lexicon Reduction Using Segment Descriptors for Arabic Handwriting Recognition

This paper presents a robust lexicon reduction technique using segment descriptors for Arabic handwritten text. The method segments an Arabic word into graphemes and adaptively generates a descriptor of the presence/absence of dots in those segments. The segmentation algorithm is based on the characteristic of Arabic script, which indicates predictable segmentations of Arabic characters. This in turn results in novel canonical segment descriptors for the lexicon entries. These descriptors are then used for lexicon reduction using a matching algorithm adapted for Arabic handwriting. Unlike other methods, features based on segment descriptors are computable for both word images and lexicon entries. Experimental results are reported on IfN/ENIT database which compare favorably with other approaches for lexicon reduction.