Script Identification of Multi-Script Documents: A Survey <s> VII. FUTURE WORK AND TRENDS <s> In this paper we describe a database that consists of handwritten English sentences. It is based on the Lancaster-Oslo/Bergen (LOB) corpus. This corpus is a collection of texts that comprise about one million word instances. The database includes 1,066 forms produced by approximately 400 different writers. A total of 82,227 word instances out of a vocabulary of 10,841 words occur in the collection. The database consists of full English sentences. It can serve as a basis for a variety of handwriting recognition tasks. However, it is expected that the database would be particularly useful for recognition tasks where linguistic knowledge beyond the lexicon level is used, because this knowledge can be automatically derived from the underlying corpus. The database also includes a few image-processing procedures for extracting the handwritten text from the forms and the segmentation of the text into lines and words. <s> BIB001 </s> Script Identification of Multi-Script Documents: A Survey <s> VII. FUTURE WORK AND TRENDS <s> We report on the creation of a database composed of images of Arabic Printed words. The purpose of this database is the large-scale benchmarking of open-vocabulary, multi-font, multi-size and multi-style text recognition systems in Arabic. The challenges that are addressed by the database are in the variability of the sizes, fonts and style used to generate the images. A focus is also given on low-resolution images where anti-aliasing is generating noise on the characters to recognize. The database is synthetically generated using a lexicon of 113’284 words, 10 Arabic fonts, 10 font sizes and 4 font styles. The database contains 45’313’600 single word images totaling to more than 250 million characters. Ground truth annotation is provided for each image. The database is called APTI for Arabic Printed Text Images. <s> BIB002 </s> Script Identification of Multi-Script Documents: A Survey <s> VII. FUTURE WORK AND TRENDS <s> This paper presents the results of the second test phase of the RIMES evaluation campaign. The latter is the first large-scale evaluation campaign intended to all the key players of the handwritten recognition and document analysis communities. It proposes various tasks around recognition and indexing of handwritten letters such as those sent by postal mail or fax by individuals to companies or administrations. In this second evaluation test, automatic systems have been evaluated on three themes: layout analysis, handwriting recognition and writer identification. The databases used are part of the RIMES database of 5605 real mails completely annotated as well as secondary databases of isolated characters and handwritten words (250,000 snippets). The paper reports on protocols and gives the results obtained in the campaign.(RIMES : Reconnaissance et Indexation de donnees Manuscrites et de fac similES / Recognition and Indexing of handwritten documents and faxes) <s> BIB003 </s> Script Identification of Multi-Script Documents: A Survey <s> VII. FUTURE WORK AND TRENDS <s> 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. <s> BIB004 </s> Script Identification of Multi-Script Documents: A Survey <s> VII. FUTURE WORK AND TRENDS <s> This paper presents a new offline dataset called the Qatar University Writer Identification dataset (QUWI). This dataset contains both Arabic and English handwritings and can be used to evaluate the performance of offline writer identification systems. It consists of handwritten documents of 1017 volunteers of different ages, nationalities, genders and education levels. The writers were asked to copy a specific text and to generate a random text, which allows the dataset to be used for both text-dependent and text-independent writer identification tasks. We describe the gathering and processing steps and define several evaluation tasks regarding the use of this dataset. <s> BIB005 </s> Script Identification of Multi-Script Documents: A Survey <s> VII. FUTURE WORK AND TRENDS <s> This paper presents a real database for the Arabic printed text recognition, APTID / MF (Arabic Printed Text Image Database / Multi-Font).This database can be used to evaluate the system that recognizes Arabic printed texts with an open vocabulary. APTID / MF may be also used for research in word segmentation and font identification. APTID / MF is obtained from 387 pages of Arabic printed documents scanned with grayscale format and 300 dpi resolutions. From this documents, 1,845 text-blocks have been extracted. In addition ground truth file is provided for each texts-block. APTID / MF also includes an Arabic printed character image dataset made up of 27,402 samples. The database is freely available to interested researchers. <s> BIB006 </s> Script Identification of Multi-Script Documents: A Survey <s> VII. FUTURE WORK AND TRENDS <s> Font recognition is useful for improving optical text recognition systems' accuracy and time, and to restore the documents' original formats. This paper addresses a need for Arabic font recognition research by introducing an Arabic font recognition database consisting of 40 fonts, 10 sizes (ranging from 8 to 24 points) and 4 styles (viz. normal, bold, italic, and bold-italic). The database is split into three sets (viz. training, validation, and testing). The database is freely available to researchers.11Corresponding author may be contacted to get access to the database. Moreover, we introduce a baseline font recognition system for benchmarking purposes, and report identification rates on our KAFD database and the Arabic Printed Text Image (APTI) database with 20 and 10 fonts, respectively. The best recognition rates are achieved using log-Gabor filters. Introduces KAFD database for Arabic font recognition research.It consists of 40 fonts, 10 sizes, and 4 styles with 100, 200, 300, and 600dpi resolutions, respectively.It presents our font recognition results using 20 fonts, 10 sizes and 4 styles.Several features are implemented, best results are achieved with log-Gabor filters.Applied our features to a second database of 10 fonts and compared results with published work. <s> BIB007 </s> Script Identification of Multi-Script Documents: A Survey <s> VII. FUTURE WORK AND TRENDS <s> This paper introduces a new offline handwriting database that was developed to be employed in performance evaluation, result comparison and development of new methods related to handwriting analysis and recognition. The database can particularly be used for signature verification, writer recognition and writer demographics classification. In addition, the database also supports isolated digit recognition, digit/text segmentation and recognition and similar related tasks. The database comprises 600 Arabic and 600 French text samples, 1300 signatures and 21,000 digits. 100 Algerian individuals coming from different age groups and educational backgrounds contributed to the development of database by providing a total of 1300 forms. The database is also accompanied with ground truth data supporting the evaluation of the aforementioned tasks. The main contribution of the database is providing a multi-script platform where same authors contributed samples in French and Arabic. It would be interesting to explore applications like writer recognition and writer demographics classification in a multi-script environment. <s> BIB008 </s> Script Identification of Multi-Script Documents: A Survey <s> VII. FUTURE WORK AND TRENDS <s> This paper presents the achievements of anexperimental project called Maurdor (Moyens AUtomatises deReconnaissance de Documents ecRits - Automatic Processingof Digital Documents) funded by the French DGA that aims atimproving processing technologies for handwritten and typewritten documents in French, English and Arabic. The first part describes the context and objectives of the project. The second part deals with the challenge of creating a realistic corpus of 10,000 annotated documents to support the efficient development and evaluation of processing modules. The third part presents the organisation, metric definition and results of the Maurdor International evaluation campaign. The last part presents the Maurdor demonstrator with a functional and technical perspective. <s> BIB009 </s> Script Identification of Multi-Script Documents: A Survey <s> VII. FUTURE WORK AND TRENDS <s> With the rapid increase of transnational communication and cooperation, people frequently encounter multilingual scenarios in various situations. In this paper, we are concerned with a relatively new problem: script identification at word or line levels in natural scenes. A large-scale dataset with a great quantity of natural images and 10 types of widely-used languages is constructed and released. In allusion to the challenges in script identification in real-world scenarios, a deep learning based algorithm is proposed. The experiments on the proposed dataset demonstrate that our algorithm achieves superior performance, compared with conventional image classification or script identification methods, including as the original CNN architecture, LLC and GLCM. <s> BIB010
Although in the last twenty years there have been many advances in the field of script identification, a great deal of work is still necessary to improve accuracy and efficiency of script identification systems. Some of the most valuable directions of research are here addressed. The first point is that, as Table 3 shows, many researchers constructed database/datasets by themselves collecting data in laboratory according to the requirement of their research work. These datasets are different in type, size, scanning resolution and image format. Besides, these datasets are not publicly available. Therefore, specific work is necessary to define some standard data formats and to realize and release public datasets for script identification. Table 7 summarizes some of the public datasets that have been considered by the script identification research community. Most of these databases are referred to research in the field of OCR BIB002 , BIB006 , handwriting recognition BIB003 , BIB007 , BIB004 , document analyzing BIB004 , writer identification and classification BIB005 , BIB003 , BIB001 , BIB004 , signature recognition and verification BIB008 . Only few public databases are specifically devoted to script identification BIB010 , BIB009 , BIB006 . Moreover, databases for script identification are limited in terms of script/language type, font types and sizes, lack degraded/noisy images, etc.. In particular, many databases are devoted to specific scripts, such as English, Chinese, Arabic, German, French, Japanese, Korean, Devanagari, Bangla, whereas no databases are available for research on other scripts. Of course, along with the expansion of research on script identification, the blank of some scripts in this area will be amended in the future work. Furthermore, most of researches is based on offline script identification technology, but there are only few reports about online script identification technology. With the spreading of PDAs and smartphones, the demand of online script identification technology is increasing. At the same time, for everyday useful aims, also automatic translation should be considered. Just think to a tourist needing for a fast automatic translation of a signboard. Of course the development of online script identification systems will require a great amount of work and it is an extraordinary challenge for the research community.