Imran Siddiqi

Bio: Imran Siddiqi is an academic researcher from Bahria University. The author has contributed to research in topics: Handwriting & Feature extraction. The author has an hindex of 24, co-authored 111 publications receiving 1814 citations. Previous affiliations of Imran Siddiqi include University of the Sciences & Paris Descartes University.

Comparison of Niblack inspired binarization methods for ancient documents

Abstract: In this paper, we present a new sliding window based local thresholding technique 'NICK' and give a detailed comparison of some existing sliding-window based thresholding algorithms with our method. The proposed method aims at achieving better binarization results, specifically, for ancient document images. NICK has been inspired from the Niblack's binarization method and exhibits its robustness and effectiveness when evaluated on low quality ancient document images.

Writer identification using texture descriptors of handwritten fragments

Abstract: This paper presents a texture based approach for identification of writers from offline images of handwriting. Contrary to the classical texture based techniques which extract texture information at page or block level, we exploit the texture at a very small observation scale. The proposed technique divides a given handwriting into small fragments and considers each fragment as a texture. Texture descriptors including histograms of Local Binary Patterns (LBP), Local Ternary Patterns (LTP) and Local Phase Quantization (LPQ) are then computed from these fragments. The writer of a document is characterized by the set of histograms calculated from all the fragments in the writing. Two writings are compared by computing the distance between the descriptors of their writing fragments. The technique evaluated on IFN/ENIT and IAM databases comprising handwritten text in Arabic and English, respectively, realized high identification rates.

A Set of Chain Code Based Features for Writer Recognition

Abstract: This communication presents an effective method for writer recognition in handwritten documents. We have introduced a set of features that are extracted from the contours of handwritten images at different observation levels. At the global level, we extract the histograms of the chain code, the first and second order differential chain codes and, the histogram of the curvature indices at each point of the contour of handwriting. At the local level, the handwritten text is divided into a large number of small adaptive windows and within each window the contribution of each of the eight directions (and their differentials) is counted in the corresponding histograms. Two writings are then compared by computing the distances between their respective histograms. The system trained and tested on two different data sets of 650 and 225 writers respectively, exhibited promising results on writer identification and verification.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Text Detection and Recognition in Imagery: A Survey

Abstract: This paper analyzes, compares, and contrasts technical challenges, methods, and the performance of text detection and recognition research in color imagery It summarizes the fundamental problems and enumerates factors that should be considered when addressing these problems Existing techniques are categorized as either stepwise or integrated and sub-problems are highlighted including text localization, verification, segmentation and recognition Special issues associated with the enhancement of degraded text and the processing of video text, multi-oriented, perspectively distorted and multilingual text are also addressed The categories and sub-categories of text are illustrated, benchmark datasets are enumerated, and the performance of the most representative approaches is compared This review provides a fundamental comparison and analysis of the remaining problems in the field

A review on the long short-term memory model

Abstract: Long short-term memory (LSTM) has transformed both machine learning and neurocomputing fields. According to several online sources, this model has improved Google’s speech recognition, greatly improved machine translations on Google Translate, and the answers of Amazon’s Alexa. This neural system is also employed by Facebook, reaching over 4 billion LSTM-based translations per day as of 2017. Interestingly, recurrent neural networks had shown a rather discrete performance until LSTM showed up. One reason for the success of this recurrent network lies in its ability to handle the exploding/vanishing gradient problem, which stands as a difficult issue to be circumvented when training recurrent or very deep neural networks. In this paper, we present a comprehensive review that covers LSTM’s formulation and training, relevant applications reported in the literature and code resources implementing this model for a toy example.

ICDAR 2009 Document Image Binarization Contest (DIBCO 2009)

Abstract: DIBCO 2009 is the first International Document Image Binarization Contest organized in the context of ICDAR 2009 conference. The general objective of the contest is to identify current advances in document image binarization using established evaluation performance measures. This paper describes the contest details including the evaluation measures used as well as the performance of the 43 submitted methods along with a short description of each method.