Gold Standard B angla OCR Dataset: An In-Depth Look at Data Preprocessing and Annotation Processes (2023)

Abstract: This research paper focuses on developing an improved Bangla Optical Character Recognition (OCR) system, addressing the challenges posed by the complexity of Bangla text structure, diverse handwriting styles, and the scarcity of comprehensive datasets. Leveraging recent advancements in Deep Learning and OCR techniques, we anticipate a significant enhancement in the performance of Bangla OCR by utilizing a large and diverse collection of labeled Bangla text image datasets. This study introduces the most extensive gold standard corpus for Bangla characters and words, comprising over 4 million human-annotated images. Our dataset encompasses various document types, such as Computer Compose, Letterpress, Typewriters, Outdoor Banner-Poster, and Handwritten documents, gathered from diverse sources. The entire corpus has undergone meticulous human annotation, employing a controlled annotation procedure consisting of three-step annotation and one-step validation, ensuring adherence to gold standard criteria. This paper provides a comprehensive overview of the complete data collection procedure. The ICT Division, Government of the People’s Republic of Bangladesh, will make the dataset publicly available, facilitating further research and development in Bangla OCR and related domains.

## introduction optical character recognition (ocr) technology has revolutionized the automation of data extraction from printed or handwritten text, enabling the conversion of scanned documents or image files into machine-readable formats for efficient data processing and information retrieval. while ocr applications have been extensively developed for various languages, such as english, datasets designed explicitly for the bengali language have been limited. existing datasets like uber-text @xcite have 110k images and 4.84 text instances per image on average, roadtext-1k @xcite have 1000 video clips of driving with annotations for text bounding boxes and transcriptions in every frame, tex-tocr @xcite have 900k annotated words collected on real images, brno @xcite ) contains 19725 photographs and more than 500k text lines with precise transcriptions, and coco-text @xcite ) contains over 173k text annotations in over 63k images, have primarily focused on english language ocr tasks. in the bengali language, datasets for ocr tasks have mainly focused on isolated character recognition @xcite , with examples such as banglalekha-isolated @xcite , ekushnet @xcite and @xcite . some authors also tried to recognize only digits @xcite . ocr technology requires methods, algorithms, and datasets to accurately recognize characters from continuous text with complex layouts. this is why an actual implementation of ocr in the bengali language has yet to be presented. some ocr technology in the bengali language is introduced for computer-composed printed documents and character recognition on some handwritten documents. but a fully-featured bengali ocr with character and continuous word recognition for computer compose, handwritten, and other forms of documents has yet to be presented. one of the major reasons for not presenting such an ocr application is the lack of a perfect dataset to build an efficient ocr application. to address this gap, we present a novel and extensive dataset that includes over 4 million annotated words and characters, covering various forms of bengali script. the primary objective of this research contribution is to provide a high-quality dataset that facilitates the advancement of bangla ocr technology and accelerates research and development in bengali language processing. by offering a comprehensive dataset encompassing different document types and writing styles, we aim to support the creation of accurate and robust ocr systems tailored for low research languages, specifically for bengali. the availability of this dataset is expected to foster significant advancements in bengali ocr research. researchers and developers can utilize this dataset to train and evaluate ocr models, thereby enhancing the accuracy and performance of ocr systems for the bengali language, which can lead to the improvement of technology advancements like document digitization, large-scale text analysis, linguistic studies, and historical research as well as socially impactful application like cultural heritage preservation, cross-linguistic studies, and community engagement. ## literature review text recognition has gained significant importance in recent years, particularly for extracting information from existing written documents. while previous methods relied on manual composition, the development of ocr technology has provided automated solutions, improving accuracy and efficiency in data extraction. various ocr systems have been developed for formal text in the context of the english language ocr, but recognizing handwritten material remains challenging. the iam-database @xcite is a collection of english sentence corpus containing texts that comprehend about one million word instances. they also include some image-processing procedures for extracting and segmenting the handwritten text into lines and words. for offline handwriting recognition over the international arabic recognition competition dataset, @xcite represents a multidimensional recurrent neural network with the combination of connectionist temporal classification, which outperformed all entries with an accuracy of 91.4%. a dataset of 4,587 arabic articles with 8,994 images is presented by @xcite for arabic printed documents. @xcite focused on constructing an ocr system for latin letters. from register 12 of pope honorii iii, a proprietary crowdsourcing platform employed high-resolution (300 dpi, 2136 × 2697 pixels) scans of 30 pages to annotate a corpus of latin letters. in terms of data collection methods, the use of mobile applications has gained popularity due to their ease and efficiency. azad @xcite showed a universal way to collect and process handwritten data from any language. using that method, @xcite and @xcite created two datasets containing 673,482 characters. @xcite collected a dataset containing 5880 characters for ocr in non-latin characters, specifically japanese characters, using mobile apps. they trained various models with the collected dataset and tesseract ocr tools. for the bangla language, banglalekha-isolated @xcite has a collection of 166,105 handwritten character images having 84 different characters comprising 50 bangla basic characters, 10 bangla numerals, and 24 selected compound characters. ekush (rabby et al., 2019) is a collection of bangla modifiers, vowels, consonants, compound letters, and numerical digits summing 367,018 isolated handwritten characters written by 3086 unique writers. mentioned two of the datasets are for handwritten documents and the collection of character-level data. a character level but for printed documents image corpus is presented by rifat et al. (2021) where they presented a collection of machine annotated eight and a half million bangla characters. a word label synthetic printed dataset is presented by roy et al. (2023), which contains 2 million sample images varied in fonts, domain, and vocabulary size. also, shihab et al. (2023), a large-scale document layout analysis dataset having 33,695 human-annotated document samples for bangla documents, will enrich the performance of the bangla ocr system. the reviewed literature demonstrates the advancements and challenges in ocr technology for various languages, including english and urdu. however, more research needs to be conducted on bengali ocr systems. the research presented in this paper aims to address this gap by providing a comprehensive dataset and proposing innovative methodologies for bengali ocr. the following section will present the methodology employed in the data collection and annotation process, followed by a detailed dataset analysis in subsequent sections. ## data collection methodology the dataset comprises various types of bengali written documents, segmented into character and word images. our objective is to create a comprehensive dataset for bangla written documents, catering to the application of ocr and facilitating research in related fields. therefore, we aimed to cover all aspects of data sources used in bangla document writing. ## experimental evaluation the presented dataset is a part of the ocr project under the ict division, government of the people's republic of bangladesh. due to the confidentiality of the assignment, the dataset has minimal access to use for building or testing existing models. however, one of a small subset of this data is provided to a research and development lab for evaluating the dataset. they have tested their crnn-vds model with vds character representation (roy et al., 2023) using our computer compose, letterpress, and typewriters data. crnn-vds is trained on a large-scale synthetic dataset having 2 million samples. ## discussion and conclusion in this research paper, we have presented the development of a monumental bangla ocr corpus, encompassing various document types, including computer-compose, typewriter, letterpress, handwriting, and outdoor scene text. the data collection process involved meticulous steps, including document collection, scanning, cropping, annotation, and validation. throughout the project, we focused on maintaining the highest quality standards for the collected data. however, our efficient team successfully managed this challenging process by implementing well-organized instructions and thorough supervision, ensuring minimal chances of errors. we encountered some challenges in the segmentation and annotation phases. auto-cropping segmentation faced difficulties with noisy documents and handwriting quality, as the quality of the paper significantly affected the legibility of the handwriting. setting a static threshold value was also challenging, considering the variation in handwriting dimensions and stroke sizes. consequently, we switched to manual cropping to ensure accurate results. in the annotation process, we faced complications, particularly with our initial android application. however, we learned from this experience and later assembled teams with efficient and experienced supervision. this adjustment significantly reduced errors and improved the overall quality of annotations. the resulting bangla ocr corpus presented in this paper is the largest and most diverse dataset available for the bangla language. its comprehensive coverage of various document types and sources makes it a valuable resource for ocr research and development. researchers can categorize the corpus based on document type, words, and characters to suit their specific needs. this corpus will be an invaluable asset to the bangla language research community. we anticipate that the research community will leverage this corpus to advance the field of bangla ocr, leading to enhanced language understanding and accessibility in the digital era. its availability will enable advancements in bengali ocr technology, serving as a benchmark for evaluating and improving ocr algorithms. moreover, it will facilitate the training and testing of ocr models designed explicitly for the bangla language.