An Examination of Character Recognition on ID card using Template Matching Approach

AbstractIdentification card (ID cards) becomes the main reference in obtaining information of a citizen. Some business sectors require the information contained in the ID card to perform the registration process. In general, the registration process is still using a form to be filled in accordance with the data on the ID card, which will then be converted into digital data by means of retyping the information. The purpose of this research is to create a character recognition system on the ID card where the character recognition process included into four stages: pre-processing, text-area extraction, segmentation and recognition. The experiment includes some tests of greyscale, binarization and segmentation algorithm, as well as the combination of those algorithms. Text area extractor showed satisfactory results of identifying text-area on the ID card, which can scope all the entire area that consist of text. In the segmentation stage, approximately 93% of character can be cut off correctly. The actual character will be mapped to the template character using two algorithms where the division grid of each of them is different. Nevertheless, the recognition process of applying the template method matching still needs to be improved back

MIDV-2019: Challenges of the modern mobile-based document OCR

Recognition of identity documents using mobile devices has become a topic of a wide range of computer vision research. The portfolio of methods and algorithms for solving such tasks as face detection, document detection and rectification, text field recognition, and other, is growing, and the scarcity of datasets has become an important issue. One of the openly accessible datasets for evaluating such methods is MIDV-500, containing video clips of 50 identity document types in various conditions. However, the variability of capturing conditions in MIDV-500 did not address some of the key issues, mainly significant projective distortions and different lighting conditions. In this paper we present a MIDV-2019 dataset, containing video clips shot with modern high-resolution mobile cameras, with strong projective distortions and with low lighting conditions. The description of the added data is presented, and experimental baselines for text field recognition in different conditions. The dataset is available for download at ftp://smartengines.com/midv-500/extra/midv-2019/.Comment: 6 pages, 3 figures, 3 tables, 18 references, submitted and accepted to the 12th International Conference on Machine Vision (ICMV 2019

Advanced Hough-based method for on-device document localization

The demand for on-device document recognition systems increases in conjunction with the emergence of more strict privacy and security requirements. In such systems, there is no data transfer from the end device to a third-party information processing servers. The response time is vital to the user experience of on-device document recognition. Combined with the unavailability of discrete GPUs, powerful CPUs, or a large RAM capacity on consumer-grade end devices such as smartphones, the time limitations put significant constraints on the computational complexity of the applied algorithms for on-device execution. In this work, we consider document location in an image without prior knowledge of the document content or its internal structure. In accordance with the published works, at least 5 systems offer solutions for on-device document location. All these systems use a location method which can be considered Hough-based. The precision of such systems seems to be lower than that of the state-of-the-art solutions which were not designed to account for the limited computational resources. We propose an advanced Hough-based method. In contrast with other approaches, it accounts for the geometric invariants of the central projection model and combines both edge and color features for document boundary detection. The proposed method allowed for the second best result for SmartDoc dataset in terms of precision, surpassed by U-net like neural network. When evaluated on a more challenging MIDV-500 dataset, the proposed algorithm guaranteed the best precision compared to published methods. Our method retained the applicability to on-device computations.Comment: This is a preprint of the article submitted for publication in the journal "Computer Optics