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JOURNAL OF INFORMATION SYSTEMS & OPERATIONS MANAGEMENT 58 INTRODUCTION In the past years, a great number of written documents have been digitized, using scanners or different types of specific methods. The need for creating a system that can translate given data, such as pictures, to editable written documents, has appeared. The technology created for this task is Optical Character Recognition (OCR). The problem with OCR engines is that a specific one may be good at recognizing only a specific type of scanned documents with certain characteristics - deterioration, paper quality, fonts and so on, and not always with 100% accuracy.The focus of the presented research is set on how several types of OCR engines can be applied to a dataset of input scanned documents to yield the best result. In the next sections, we will analyze the potential result of each system and based on their performance on a specific document type, a voting algorithm will be employed in which the best engine has more weight in the overall decision process. We will start by giving details about the used systems and all possible alternatives.
We will further present our system and analyze its workflow. Finally, we will present the results after the images were processed through our system.USED SYSTEMS AND RELATED WORK At the core of this paper is situated the OCR technology, which is the artificially reading process, in which image data from documents or natural scenes containing written messages is converted into text data 1.
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Modern OCR engines are powerful because they provide the above-mentioned functionality without the need for developing the code further. JOURNAL OF INFORMATION SYSTEMS & OPERATIONS MANAGEMENT 59 Tesseract Tesseract is a Google-developed OCR project, from 2006 6.
It evolved a lot during the years, starting from a simple NN-based text reader, without any support for layout analysis, into a fully-featured system, which recognizes common layouts and offers both NN and LSTM recognition support. The later versions of Tesseract support different output formats, including hOCR with layout and formatting information 9, or may even be integrated with frontends such as Ocropus 10.
JOURNAL OF INFORMATION SYSTEMS & OPERATIONS MANAGEMENT 61 RESULTS After receiving the input file, each OCR has created its own visual interpretation of the text based on the confidence level. The main scenario input test image The input file is represented by Figure 3, whilst the output OCR texts color-bordered using the specific engine confidence in Figure 4.Figure 4. The visual output generated: Tesseract (top) and Asprise (bottom) The following step was the analysis of the output files of each engine as seen in Figure 5 and their comparison to the test output in order to determinate and set the weight. The output generated by Asprise (top) and by Tesseract (bottom).
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JOURNAL OF INFORMATION SYSTEMS & OPERATIONS MANAGEMENT 62 In the last step, we combined the documents based on their given weights and obtained the final output which can be seen in Figure 6. Results after combining the results (red Tesseract and blue Asprise) A series of experiments were performed in order to assess the most appropriate weights. In figures 7 and 8 are identified some results based on the confidence level of each OCR engine. The intensity of the color gives the confidence level: green represents high confidence, red means low confidence.Figure 7. The blurred scenario input test image Figure 8.Output Tesseract (left) and Aspire (right) After analyzing more situations, it is noticed that for any level of image blur, but especially for an intense one, Asprise tends to perform better than Tesseract. One good example is presented in Figure 9.
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Output Tesseract (left) and Aspire (right). JOURNAL OF INFORMATION SYSTEMS & OPERATIONS MANAGEMENT 63 CONCLUSIONS When the results of several OCR engines were compared, it can be observed that each one of them has its own shortcomings when dealing with the degradation of the input files.
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