Enhanced Handwritten Document Recognition Using Confusion Matrix Analysis

The handwritten Multilanguage phase is the preprocessing phase that improves the image quality for better identification in the system. The main goals of preprocessing are diodes, noise suppression and line cancellation. After word processing, various attribute extraction techniques are used to process attribute properties for the identification process. Smoothing plays an important role in character recognition. The partitioning process in the word distribution strategy can be divided into global and local texts. The writer does not use this header line to write the text which creates a problem for skew correction, classification and recognition. The dataset used are HWSC and TST1. The tensor flow method is used to estimate the consistency of confusion matrix for the enhancement of the text recognition .The accuracy of the proposed method is 98%.

Artificial Intelligence in Automated Bookkeeping: A Value-added Function for Small and Medium Enterprises

Bookkeeping plays a vital role in dealing with records of day-to-day financial transactions from invoices until payment. It is also a method of documenting all company transactions in order to create a collection of accounting documents. Studies show that an evolution of bookkeeping management from manual record keeping to electronic record keeping had simplified most burden of bookkeepers as well as more reliable and accurate. Bookkeeping includes, in particular, classifying items correctly and entering financial details into an accounting system. However, with the rise of artificial intelligence, automated bookkeeping system is common to large businesses tasks at real time with hassle free. The system will function more than just journal management but also a decision-making tool to any businesses. Despite the benefits of the system, many small and medium enterprises especially in Malaysia still hesitate to implement the system. Artificial intelligence will further improve automated bookkeeping making it simpler and efficient for all levels of businesses. This paper presents an Artificial Intelligence perspective and methods used in automated bookkeeping focuses on invoices processes such as Optical Character Recognition (OCR), for document recognition, machine learning and auto journal record entries. Besides that, its challenges to be implemented in small and medium enterprise. The result of these studies highlighted benefits in the automated bookkeeping process to suit Malaysian small and medium enterprises. Future work will look at the suggested intelligence features to be implemented for a more efficient automated bookkeeping for small and medium enterprise.

A Method of Image Quality Assessment for Text Recognition on Camera-Captured and Projectively Distorted Documents

In this paper, we consider the problem of identity document recognition in images captured with a mobile device camera. A high level of projective distortion leads to poor quality of the restored text images and, hence, to unreliable recognition results. We propose a novel, theoretically based method for estimating the projective distortion level at a restored image point. On this basis, we suggest a new method of binary quality estimation of projectively restored field images. The method analyzes the projective homography only and does not depend on the image size. The text font and height of an evaluated field are assumed to be predefined in the document template. This information is used to estimate the maximum level of distortion acceptable for recognition. The method was tested on a dataset of synthetically distorted field images. Synthetic images were created based on document template images from the publicly available dataset MIDV-2019. In the experiments, the method shows stable predictive values for different strings of one font and height. When used as a pre-recognition rejection method, it demonstrates a positive predictive value of 86.7% and a negative predictive value of 64.1% on the synthetic dataset. A comparison with other geometric quality assessment methods shows the superiority of our approach.

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 docu-ment 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.

Weighted combination of per-frame recognition results for text recognition in a video stream

The scope of uses of automated document recognition has extended and as a result, recognition techniques that do not require specialized equipment have become more relevant. Among such techniques, document recognition using mobile devices is of interest. However, it is not always possible to ensure controlled capturing conditions and, consequentially, high quality of input images. Unlike specialized scanners, mobile cameras allow using a video stream as an input, thus obtaining several images of the recognized object, captured with various characteristics. In this case, a problem of combining the information from multiple input frames arises. In this paper, we propose a weighing model for the process of combining the per-frame recognition results, two approaches to the weighted combination of the text recognition results, and two weighing criteria. The effectiveness of the proposed approaches is tested using datasets of identity documents captured with a mobile device camera in different conditions, including perspective distortion of the document image and low lighting conditions. The experimental results show that the weighting combination can improve the text recognition result quality in the video stream, and the per-character weighting method with input image focus estimation as a base criterion allows one to achieve the best results on the datasets analyzed.

Algorithm for choosing the best frame in a video stream in the task of identity document recognition

During the process of document recognition in a video stream using a mobile device camera, the image quality of the document varies greatly from frame to frame. Sometimes recognition system is required not only to recognize all the specified attributes of the document, but also to select final document image of the best quality. This is necessary, for example, for archiving or providing various services; in some countries it can be required by law. In this case, recognition system needs to assess the quality of frames in the video stream and choose the “best” frame. In this paper we considered the solution to such a problem where the “best” frame means the presence of all specified attributes in a readable form in the document image. The method was set up on a private dataset, and then tested on documents from the open MIDV-2019 dataset. A practically applicable result was obtained for use in recognition systems.