Histogram Equalization Implementation in the Preprocessing Phase on Optical Character Recognition

A 2014 report from Digital Marketing Philippines stated that the number of web applications with visual content as their main product has increased significantly. Image processing technology has also undergone significant growth. One example of this is optical character recognition (OCR), which can convert the text on an image to plain text. However, a problem occurs when the image has low contrast and low exposure, which potentially results in information being hidden in the image. To address this problem, histogram equalization is used to enhance the image’s contrast so the hidden information can be shown. Similar to X-ray scanning used in the medical field, histogram equalization processes scanned images that have low brightness and low contrast. In this study, histogram equalization was successfully implemented using OCR preprocessing. The test was done with a dataset that contains dark background images with low light text; the successful outcome resulted in the ability to show 74.95% of the information hidden in the image.

Contrast enhancement; Histogram equalization; Image processing; Information hiding; Optical character recognition

ready to be processed. One important preprocessing step is the improvement of the image with dark and light colors.

No OCR reader can read all the conditions of an image perfectly (Abbyy-developers.eu, 2015). Various tests have been conducted with a variety of datasets (iapr-tc11.org, 2015). However, the majority of the images contained in the dataset collection have had fairly good image conditions (quite bright and good contrast), and they have supported the delivery of clear information from the image (no hidden information). Therefore, to ensure that OCR can be used for a variety of image conditions, improvements in the color, light, and dark elements used in an image (color adjustment) are needed. Some of the methods commonly used to improve the condition of an image include histogram equalization, Wiener filtering, median filtering, decorrelation stretch, and unsharp mask filters (MathWorks.com, 2016).

In the present study, histogram equalization was selected as the image enhancement method because it is similar to X-ray scanning that is used in the medical field to scan organs. Histogram equalization was used to clarify the background and the object of regional differences, and to identify information hidden in the images due to low light and low contrast (Akhlis & Sugiyanto, 2011). In addition, this method is considered fairly common and easy to apply to an image (Alginahi, 2010).

The research methodology used in the present study was implemented in the following stages.

3.1.    Learning and Consulting

A literature review was conducted to identify previous research on this topic. This phase was done by collecting supporting data associated with the present research study. The data collection was done by reviewing various types of scientific work, such as books, journals, and articles. This phase is carried out so that the research study can be conducted in accordance with the provisions presented in previous studies in order to produce a valid conclusion.

3.2.    Designing the Application and Identifying the Analysis Requirements

After collecting the supporting data, we conducted a needs analysis to determine the standard to be used in research. In addition to the analysis, we designed the application that will be used as media in the study. This design resulted in several documents, namely flowcharts and the structure of the storage table. Figure 6 shows the procedures we undertook to implement the histogram equalization method used in this research.

Figure 6 Flowchart of the histogram equalization method used in the study

3.3.    Programming

After establishing the design and determining the analysis requirements, we programmed the application. The application served as a media liaison between the users and the systems used for implementation. The application was designed so users can enter their own samples and obtain rapid analysis of them.

3.4.    Testing and Debugging

After completing the programming stage, the application was tested. The tests were conducted using all the functions that were made in the programming stage (Stage 3). In addition to testing all the functions, we also performed tests on all the possibilities that could occur in the application when it is used by a user.

3.5.    Collecting Samples

After the testing was completed, the application was deemed ready to be used in real-time by users. Therefore, we collected the samples. The sample collection was done by looking for a random sample in accordance with the needs generated in the designing the application and identifying the analysis requirements (Stage 2).

3.6.    Analysis of the Test Results

After obtaining a variety of samples from users, we further analyzed the data to determine the impact of the implementation results and conclusions from the application of the histogram equalization method used in the study.

3.7.    Report and Documentation

To complete the research activities, we wrote a report to document the procedures and findings of the research study.

After designing and implementing the program, further testing was done using the samples that were collected. The samples collected by searching for dark background samples tended to be black, have low contrast, and were not crowded. The analysis aimed to determine if the image conditions were in accordance with the application of histogram equalization in general. The tests were performed offline using a sample dataset, which contained images with a dark background picture in dominant black (RGB # 000000). Then, some texts were added on each image. Selection of the color of the text was done by adding a hexadecimal value 10 for each component of red, green, and blue (RGB) of the background; for example, RGB #000000 to RGB #101010. The test results are presented in Table 1.

Table 1 The testing results