These days, OCR and digital image processing technology are rapidly developing and there are many application areas in research and industry. This thesis presents a method to produce a better and reliable recognition by manipulating the output of OCR process in domain specific word recognition tasks. The output of OCR is improved by two post-processing steps: the tokenization and the extraction of correct word using dictionaries. The tokenization is a process where texts retrieved by OCR are seperated into word tokens. Then the tokens are compared with english and proprietary dictionaries in sequence. English dictionary is used to convert the word tokens into correct words candidates, while proprietary dictionary is used as a guide to select only meaningful words in the domain specific task. The practicality of the proposed approach was demonstrated in the task of text recognition of the ingredients list printed on the cover of the packaged foods. Based on the uploaded image of packaged food, the system performs OCR to get the editable texts. The editable texts are then tokenized into word tokens before the post-processing steps. Word tokens are then converted into correct words by the processes implicates the use of dictionaries.The result of these combined approaches on the system are reliable as it gives an accurate result of the ingredients without useless characters and nonessential ingredients.