Recognizing handwritten Arabic words using grapheme segmentation and recurrent neural networks

The Arabic alphabet is used in around 27 languages, including Arabic, Persian, Kurdish, Urdu, and Jawi. Many researchers have developed systems for recognizing cursive handwritten Arabic words, using both holistic and segmentation-based approaches. This paper introduces a system that achieves high accuracy using efficient segmentation, feature extraction, and recurrent neural network (RNN). We describe a robust rule-based segmentation algorithm that uses special feature points identified in the word skeleton to segment the cursive words into graphemes. We show that careful selection from a wide range of features extracted during and after the segmentation stage produces a feature set that significantly reduces the label error. We demonstrate that using same RNN recognition engine, the segmentation approach with efficient feature extraction gives better results than a holistic approach that extracts features from raw pixels. We evaluated this segmentation approach against an improved version of the holistic system MDLSTM that won the ICDAR 2009 Arabic handwritten word recognition competition. On the IfN/ENIT database of handwritten Arabic words, the segmentation approach reduces the average label error by 18.5 %, the sequence error by 22.3 %, and the execution time by 31 %, relative to MDLSTM. This approach also has the best published accuracies on two IfN/ENIT test sets.     

印刷维吾尔文本切割

我国新疆地区使用的维吾尔文借用阿拉伯文字母书写。因为阿拉伯文字母自身书写的特点,造成维文文本的切割和识别极其困难。本文在连通体分类的基础上,结合水平投影和连通体分析的方法实现维文文本的文字行切分和单词切分。然后定位单词基线位置,计算单词轮廓和基线的距离,寻找所有可能的切点实现维文单词过切割,最后利用规则合并过切分字符。实验结果表明,字符切割准确率达到99 %以上。     

Region growing based segmentation algorithm for typewritten and handwritten text recognition

This paper presents a new technique of high accuracy to recognize both typewritten and handwritten English and Arabic texts without thinning. After segmenting the text into lines (horizontal segmentation) and the lines into words, it separates the word into its letters. Separating a text line (row) into words and a word into letters is performed by using the region growing technique (implicit segmentation) on the basis of three essential lines in a text row. This saves time as there is no need to skeletonize or to physically isolate letters from the tested word whilst the input data involves only the basic information—the scanned text. The baseline is detected, the word contour is defined and the word is implicitly segmented into its letters according to a novel algorithm described in the paper. The extracted letter with its dots is used as one unit in the system of recognition. It is resized into a 9 × 9 matrix following bilinear interpolation after applying a lowpass filter to reduce aliasing. Then the elements are scaled to the interval [0,1]. The resulting array is considered as the input to the designed neural network. For typewritten texts, three types of Arabic letter fonts are used—Arial, Arabic Transparent and Simplified Arabic. The results showed an average recognition success rate of 93% for Arabic typewriting. This segmentation approach has also found its application in handwritten text where words are classified with a relatively high recognition rate for both Arabic and English languages. The experiments were performed in MATLAB and have shown promising results that can be a good base for further analysis and considerations of Arabic and other cursive language text recognition as well as English handwritten texts. For English handwritten classification, a success rate of about 80% in average was achieved while for Arabic handwritten text, the algorithm performance was successful in about 90%. The recent results have shown increasing success for both Arabic and English texts.     

A SVM-based cursive character recognizer

This paper presents a cursive character recognizer, a crucial module in any cursive word recognition system based on a segmentation and recognition approach. The character classification is achieved by using support vector machines(SVMs) and a neural gas. The neural gas is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not. Once this is done for every letter, the character recognition is performed by SVMs. A database of 57 293 characters was used to train and test the cursive character recognizer. SVMs compare notably better, in terms of recognition rates, with popular neural classifiers, such as learning vector quantization and multi-layer-perceptron. SVM recognition rate is among the highest presented in the literature for cursive character recognition.     

Large vocabulary recognition of on-line handwritten cursive words

This paper presents a writer independent system for large vocabulary recognition of on-line handwritten cursive words. The system first uses a filtering module, based on simple letter features, to quickly reduce a large reference dictionary (lexicon) to a more manageable size; the reduced lexicon is subsequently fed to a recognition module. The recognition module uses a temporal representation of the input, instead of a static two-dimensional image, thereby preserving the sequential nature of the data and enabling the use of a Time-Delay Neural Network (TDNN); such networks have been previously successful in the continuous speech recognition domain. Explicit segmentation of the input words into characters is avoided by sequentially presenting the input word representation to the neural network-based recognizer. The outputs of the recognition module are collected and converted into a string of characters that is matched against the reduced lexicon using an extended Damerau-Levenshtein function. Trained on 2,443 unconstrained word images (11 k characters) from 55 writers and using a 21 k lexicon we reached a 97.9% and 82.4% top-5 word recognition rate on a writer-dependent and writer-independent test, respectively     

Binary segmentation algorithm for English cursive handwriting recognition

Segmentation in off-line cursive handwriting recognition is a process for extracting individual characters from handwritten words. It is one of the most difficult processes in handwriting recognition because characters are very often connected, slanted and overlapped. Handwritten characters differ in size and shape as well. Hybrid segmentation techniques, especially over-segmentation and validation, are a mainstream to solve the segmentation problem in cursive off-line handwriting recognition. However, the core weakness of the segmentation techniques in the literature is that they impose high risks of chain failure during an ordered validation process. This paper presents a novel Binary Segmentation Algorithm (BSA) that reduces the risks of the chain failure problems during validation and improves the segmentation accuracy. The binary segmentation algorithm is a hybrid segmentation technique and it consists of over-segmentation and validation modules. The main difference between BSA and other techniques in the literature is that BSA adopts an un-ordered segmentation strategy. The proposed algorithm has been evaluated on CEDAR benchmark database and the results of the experiments are very promising.     

Structural analysis of Arabic handwriting: segmentation and recognition

In this paper, a structural method of recognising Arabic handwritten characters is proposed. The major problem in cursive text recognition is the segmentation into characters or into representative strokes. When we segment the cursive portions of words, we take into account the contextual properties of the Arabic grammar and the junction segments connecting the characters to each other along the writing line. The problem of overlapping characters is resolved with a contour-following algorithm associated with the labelling of the detected contours. In the recognition phase, the characters are gathered into ten families of candidate characters with similar shapes. Then a heterarchical analysis follows that checks the pattern via goal-directed feedback control.     

An image-based automatic Arabic translation system

In this paper, we present a system that automatically translates Arabic text embedded in images into English. The system consists of three components: text detection from images, character recognition, and machine translation. We formulate the text detection as a binary classification problem and apply gradient boosting tree (GBT), support vector machine (SVM), and location-based prior knowledge to improve the F1 score of text detection from 78.95% to 87.05%. The detected text images are processed by off-the-shelf optical character recognition (OCR) software. We employ an error correction model to post-process the noisy OCR output, and apply a bigram language model to reduce word segmentation errors. The translation module is tailored with compact data structure for hand-held devices. The experimental results show substantial improvements in both word recognition accuracy and translation quality. For instance, in the experiment of Arabic transparent font, the BLEU score increases from 18.70 to 33.47 with use of the error correction module.     

多字体印刷维吾尔文的切分

在许多文字识别系统中, 字符切分是预处理阶段的一部分, 其目的是从文本图象中分离出字母图象。而后才能针对切分后的每个字母进行识别。在具有连体特征的文字中, 字符切分就显得特别重要, 因为字符切分的准确与否直接影响字符的识别。维吾尔文就具有这种明显的连体特点, 本文主要讨论了采用抽取投影特征的方法, 实现了多字体维吾尔文的行切分、字切分和字符切分。     

On-line recognition of Korean characters using ART neural network and hidden Markov model

This paper proposes an efficient method for on-line recognition of cursive Korean characters. The recognition of cursive strokes and the representation of a large character set are important determinants in the recognition rate of Korean characters. To deal with cursive strokes, we classify them automatically by using an ART-2 neural network. This neural network has the advantage of assembling similar patterns together to form classes in a self-organized manner. To deal with the large character set, we construct a character recognition model by using the hidden Markov model (HMM), which has the advantages of providing an explicit representation of time-varying vector sequence and probabilistic interpretation. Probabilistic parameters of the HMM are initialized using the combination rule for Korean characters and a set of primitive strokes that are classified by the ART stroke classifier, and trained with sample data. This is an efficient means of representing all the 11,172 possible Korean characters. We tested the model on 7500 on-line cursive Korean characters and it proved to perform well in recognition rate and speed.     

基于BiLSTM-CRF的古汉语自动断句与词法分析一体化研究

古汉语信息处理的基础任务包括自动断句、自动分词、词性标注、专名识别等。大量的古汉语文本未经标点断句,所以词法分析等任务首先需要建立在断句基础之上。然而,分步处理容易造成错误的多级扩散,该文设计实现了古汉语断句与词法分析一体化的标注方法,基于BiLSTM-CRF神经网络模型在四种跨时代的测试集上验证了不同标注层次下模型对断句、词法分析的效果以及对不同时代文本标注的泛化能力。研究表明,一体化的标注方法对古汉语的断句、分词及词性标注任务的F₁值均有提升。综合各测试集的实验结果,断句任务F₁值达到78.95%,平均提升了3.5%;分词任务F₁值达到85.73%,平均提升了0.18%;词性标注任务F₁值达到72.65%,平均提升了0.35%。     

High accuracy optical character recognition using neural networkswith centroid dithering

Optical character recognition (OCR) refers to a process whereby printed documents are transformed into ASCII files for the purpose of compact storage, editing, fast retrieval, and other file manipulations through the use of a computer. The recognition stage of an OCR process is made difficult by added noise, image distortion, and the various character typefaces, sizes, and fonts that a document may have. In this study a neural network approach is introduced to perform high accuracy recognition on multi-size and multi-font characters; a novel centroid-dithering training process with a low noise-sensitivity normalization procedure is used to achieve high accuracy results. The study consists of two parts. The first part focuses on single size and single font characters, and a two-layered neural network is trained to recognize the full set of 94 ASCII character images in 12-pt Courier font. The second part trades accuracy for additional font and size capability, and a larger two-layered neural network is trained to recognize the full set of 94 ASCII character images for all point sizes from 8 to 32 and for 12 commonly used fonts. The performance of these two networks is evaluated based on a database of more than one million character images from the testing data set