Muhammad Imran Razzak

The optical character recognition of Urdu-like cursive scripts

We survey the optical character recognition (OCR) literature with reference to the Urdu-like cursive scripts. In particular, the Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nastaliq and Naskh scripts. Before detaining the OCR works, the peculiarities of the Urdu-like scripts are outlined, which are followed by the presentation of the available text image databases. For the sake of clarity, the various attempts are grouped into three parts, namely: (a) printed, (b) handwritten, and (c) online character recognition. Within each part, the works are analyzed par rapport a typical OCR pipeline with an emphasis on the preprocessing, segmentation, feature extraction, classification, and recognition. HighlightsA literature review of the Nastaliq and Naskh cursive script OCR.The peculiarities and challenges are described a priori.Printed, handwritten and online OCR efforts are being explored.Analyses based on the stages of a typical OCR pipeline.

Evaluation of cursive and non-cursive scripts using recurrent neural networks

AbstractnCharacter recognition has been widely used since its inception in applications involved processing of scanned or camera-captured documents. There exist multiple scripts in which the languages are written. The scripts could broadly be divided into cursive and non-cursive scripts. The recurrent neural networks have been proved to obtain state-of-the-art results for optical character recognition. We present a thorough investigation of the performance of recurrent neural network (RNN) for cursive and non-cursive scripts. We employ bidirectional long short-term memory (BLSTM) networks, which is a variant of the standard RNN. The output layer of the architecture used to carry out our investigation is a special layer called connectionist temporal classification (CTC) which does the sequence alignment. The CTC layer takes as an input the activations of LSTM and aligns the target labels with the inputs. The results were obtained at the character level for both cursive Urdu and non-cursive English scripts are significant and suggest that the BLSTM technique is potentially more useful than the existing OCR algorithms.n

Offline cursive Urdu-Nastaliq script recognition using multidimensional recurrent neural networks

Optical Character Recognition of cursive scripts remains a challenging task due to a large number of character shapes, inter- and intra-word overlaps, context sensitivity and diagonality of text. This paper presents an implicit segmentation based recognition system for Urdu text lines in Nastaliq script. The proposed technique relies on sliding overlapped windows on lines of text and extracting a set of statistical features. The extracted features are fed to a multi-dimensional long short term memory recurrent neural network (MDLSTM RNN) with a connectionist temporal classification (CTC) output layer that labels the character sequences. Experimental study of the proposed technique is carried out on the standard Urdu Printed Text-line Images (UPTI) database which comprises 10,000 text lines in Nastaliq font. Evaluations under different experimental settings realize promising recognition rates with a highest character recognition rate of 96.40%.

Urdu Nasta'liq text recognition system based on multi-dimensional recurrent neural network and statistical features

AbstractnCharacter recognition for cursive script like Arabic, handwritten English and French is a challenging task which becomes more complicated for Urdu Nasta’liq text due to complexity of this script over Arabic. Recurrent neural network (RNN) has proved excellent performance for English, French as well as cursive Arabic script due to sequence learning property. Most of the recent approaches perform segmentation-based character recognition, whereas, due to the complexity of the Nasta’liq script, segmentation error is quite high as compared to Arabic Naskh script. RNN has provided promising results in such scenarios. In this paper, we achieved high accuracy for Urdu Nasta’liq using statistical features and multi-dimensional long short-term memory. We present a robust feature extraction approach that extracts feature based on right-to-left sliding window. Results showed that selected features significantly reduce the label error. For evaluation purposes, we have used Urdu printed text images dataset and compared the proposed approach with the recent work. The system provided 94.97xa0% recognition accuracy for unconstrained printed Nasta’liq text lines and outperforms the state-of-the-art results.

Segmentation techniques for recognition of Arabic-like scripts: A comprehensive survey

Arabic script based text recognition system has been a popular field of research for many years that can be used in the learning and teaching process to the students and educators how to read and understand educational contents of Arabic script. The challenging nature of Arabic script recognition has attracted the attention of researchers from both industry and academic circles but these efforts have not achieved good results until now. Segmentation of Urdu script when written in Nasta’liq writing style is very difficult task due to the complexity of writing style as compare to Naskh writing style. Good segmentation is one of the reasons for high accuracy. Character segmentation has been a critical phase of the OCR process. The higher recognition rates for isolated characters as compare to results of words or connected character well illustrate the importance of segmentation. Current study investigates the recent work for character segmentation and challenges for segmentation for Arabic script based languages.

Arabic script based character segmentation: A review

Segmentation based Arabic script based languages character recognition has been a popular field of research for many years. The challenging nature of Arabic script recognition has attracted the attention of researchers from both industry and academic circles but these efforts have not achieved good results until now. Segmentation of Urdu script when written in Nastaliq writing style is very difficult task due to the complexity of writing style as compare to Naskh writing style. Good segmentation is one of reasons for high accuracy. Character segmentation has been a critical phase of the OCR process. The higher recognition rates for isolated characters as compare to results of words or connected character well illustrate the importance of segmentation. Current study investigate the recent work for character segmentation and challenges for segmentation for Arabic script based languages.

Arabic script based language character recognition: Nasta'liq vs Naskh analysis

Arabic and various Indic scripts received researcher attention after a long time of focus on East Asian and Western whereas this script is used by 1/4th of the world population by many languages in several countries. Arabic script is more complex as compared to Latin script. One of such difficulty with Arabic script is multiple writing styles. Naskh and Nastaliq are the two most commonly style adopted by Arabic script based languages. This paper compares both writing style and concludes that why work done for Naskh cannot be applied for Nastaliq writing style.

Challenges in Baseline Detection of Arabic Script Based Languages

In this chapter, we present baseline detection challenges for Arabic script based languages and targeted Nastaliq and Naskh writing style. Baseline is an important step in the OCR as it directly affects the rest of the steps and increases the performance and efficiency of character segmentation and feature extraction in OCR process. Character recognition on Arabic script is relatively more difficult than Latin text due to the nature of Arabic script, which is cursive, context sensitive and different writing style. In this paper, we provide a comprehensive review of baseline detection methods for Urdu language. The aim of the chapter is to introduce the challenges during baseline detection in cursive script languages for Nastaliq and Naskh script.

Efficient leukocyte segmentation and recognition in peripheral blood image

BACKGROUNDnBlood cell count, also known as differential count of various types of blood cells, provides valuable information in order to assess variety of diseases like AIDS, leukemia and blood cancer. Manual techniques are still used in diseases diagnosis that is very lingering and tedious process. However, machine based automatic analysis of leukocyte is a powerful tool that could reduce the human errors, improve the accuracy, and minimize the required time for blood cell analysis. However, leukocyte segmentation is a challenging process due to the complexity of the blood cell image; therefore, this task remains unresolved issue in the blood cell segmentation.nnnOBJECTIVEnThe aim of this work is to develop an efficient leukocyte cell segmentation and classification system.nnnMETHODSnThis paper presents an efficient strategy to segment cell images. This has been achieved by using Wiener filter along with Curvelet transform for image enhancement and noise elimination in order to elude false edges. We have also used combination of entropy filter, thresholding and mathematical morphology for obtaining image segmentation and boundary detection, whereas we have used back-propagation neural network for leukocyte classification into its sub classes.nnnRESULTSnAs a result, the generated segmentation results are fruitful in a sense that we have overcome the problem of overlapping cells. We have obtained 100%, 96.15%, 92.30%, 92.30% and 96.15% accuracy for basophil, eosinophil, monocyte, lymphocyte and neutrophil respectively.

Zoning Features and 2DLSTM for Urdu Text-line Recognition

Recognition of Urdu cursive script is a challenging task due to the implicit complexities associated with it. The performance of a recognition system is immensely dependent on extracted features. There are various features extraction approaches proposed in recent years. Among many, an approach based on zoning features proved to be efficient and popular. Such zoning features represent significant information with low complexity and high speed. In this paper, we used zoning features for the classification of Urdu Nastaliq text lines, with a combination of 2-Dimensional Long Short Term Memory networks (2DLSTM) as learning classifier. The proposed model is evaluated on publicly available UPTI dataset and character recognition rate of 93.39% is obtained.