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foundation:gsoc_chillara [2010/07/13 17:58] suryajith |
foundation:gsoc_chillara [2010/12/18 17:35] (current) |
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== Code == | == Code == | ||
* [[https:// | * [[https:// | ||
- | * [[http:// | ||
==Functional Specifications.== | ==Functional Specifications.== | ||
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An interface to the end user shall be provided where the user shall upload the scanned image and then get a UI where the user shall compare and correct. | An interface to the end user shall be provided where the user shall upload the scanned image and then get a UI where the user shall compare and correct. | ||
- | __Technologies__ | ||
- | |||
- | - Tesseract | ||
- | - Apache-FOP / rst2pdf / ReportLab | ||
- | |||
- | __Open Issues__ | ||
- | |||
- | <Shall be updated> | ||
__Comments__ | __Comments__ | ||
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^SMART goal^Measure^Due date^Comments^ | ^SMART goal^Measure^Due date^Comments^ | ||
| Xforms to pdf | [[http:// | | Xforms to pdf | [[http:// | ||
- | | Correction UI | Web UI with a text box and a corresponding image for every element | 28th June | (Postponed towards the end) | | + | | Tesseract integration | Tests with printed data. | 5th July | INTEGRATED: waiting for the community to check it. | |
- | | Tesseract integration | Tests with printed data. | 5th July | In Progress (Tesseract is integrated, testing | + | | Automated training |
- | | Automated training | + | | Assembly and Testing |
- | | Web UI | Making some necessary modifications to the UI | 2nd August | TO BE DONE | | + | | Correction UI | Web UI with a text box and a corresponding image for every element |
- | | Testing | + | |
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{{: | {{: | ||
+ | |||
+ | -------------- | ||
==README== | ==README== | ||
+ | **File structure of the ocr folder:** | ||
- | src: | + | config <- Global config file |
- | | + | |
- | | + | images: <- A possible storage area for the images |
- | * generateTrainingform.py | + | |
- | * parseform.py | + | layoutfiles: |
- | | + | |
- | * regions.py | + | ocrforms: <- Default storage area for the xml forms |
- | * train.py | + | |
- | * xforms2pdf.py | + | parseddata: <- Stores the parsed data |
+ | |||
+ | README <- Explains the howto | ||
+ | |||
+ | |||
+ | sahanahcr: | ||
+ | |-dataHandler.py <- A class to parse the images and dump the data | ||
+ | |-formHandler.py | ||
+ | |-functions.py | ||
+ | |-parseform.py <- A script to parse the forms | ||
+ | |-printForm.py <- A class to handle the reportlab api to print forms | ||
+ | |-regions.py <- A Class which describes a region in an image | ||
+ | |-upload.py <- A script to upload the files | ||
+ | |-urllib2_file.py <- A module which augments urllib2' | ||
+ | |-xforms2pdf.py <- Converts xforms to pdfs and uses the classes from formhandler and printform | ||
+ | |||
+ | |||
+ | tessdata: <- A folder where the necessary training info is stored to parse the scanned forms | ||
+ | |-configs | ||
+ | |-tessconfigs | ||
+ | |||
+ | training: | ||
+ | |-generatetrainingform.py <- Generates the training form | ||
+ | |-train.py <- Trains the engine and stores the training data in the tessdata folder | ||
+ | |-datafiles: | ||
+ | |-printedpdfs: | ||
+ | |||
+ | |||
+ | xmlInput: | ||
+ | ------------ | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | **Dependencies** | ||
+ | ------------ | ||
+ | | ||
+ | - Core xml libs like xml.dom.minidom and xml.sax | ||
+ | - sane on unix and twain on Windows to support scanning | ||
+ | - pyscanning (http:// | ||
+ | - Imaging-sane (http:// | ||
+ | - urllib | ||
+ | - urllib2 | ||
+ | - PIL >= 1.6 | ||
+ | |||
+ | NOTE 1: All scripts have to be run from their respective directories at the moment. | ||
+ | |||
+ | NOTE 2: All the images used are to be provided in the .tif format. | ||
+ | |||
+ | |||
+ | **USAGE** | ||
+ | |||
+ | |||
+ | __Setting up the config file :__ | ||
+ | |||
+ | [url] | ||
+ | |||
+ | url = http:// | ||
+ | |||
+ | The url to which data could be uploaded to | ||
+ | |||
+ | [tessdata] | ||
+ | |||
+ | tessdata = ../ | ||
+ | |||
+ | The folder from the sahanahcr folder where the tessdata folder is located | ||
+ | |||
+ | ------------ | ||
+ | |||
+ | __Step 1: The form generation__ | ||
+ | The forms could be generated using the xforms2pdf.py using the syntax as mentioned below. Incase the pdfname is not mentioned, it uses the uuid.pdf format to save the files. They are stored in the OCRforms folder in the main directory structure. | ||
+ | |||
+ | Usage: python xforms2pdf.py < | ||
+ | |||
+ | __Step 2: Automated training__ | ||
+ | Generation of the training forms. Uses the datainput.txt located in the " | ||
+ | |||
+ | Usage: python | ||
+ | |||
+ | The automation of the tesseract stores the necessary files in the tessdata folder with a < | ||
+ | |||
+ | Usage: python train.py < | ||
+ | |||
+ | |||
+ | __Step 3: Scan the Image or Add the Image__ | ||
+ | The Images could either be added to the folder images as described in the files structure (or be scanned directly) and the parsing of the images takes place with accordance to the the layout files located in the layout folder. The layout is chosen as per the uuid mentioned on the form as the layout file is stored in the form uuid_page.xml so everytime a page is scanned, the page number has to be specified too. < | ||
+ | |||
+ | Usage: python | ||
+ | |||
+ | The parsed data is stored in the form of an xml in the folder parseddata in the global file structure. It is stored in the < | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | TODO | ||
+ | |||
+ | | ||
+ | - Someway to deal with the files with multiple pages, a way to store the data instead of the present way of storing data in two different xmls | ||
+ | - Do windows specific improvements and tests | ||
+ | - Barcodes on each page | ||
+ | - Make generateTrainingform.py generic for all languages | ||
+ | - Improve the xforms parsing to use all the attributes of bind | ||
+ | - Check the improvement due to parsing digits and alphabets independently | ||
+ | - Try to check if individual character reading improves the accuracy rather than reading the entire string | ||
+ | - Should check if multiple fields have been selected for a select1 element | ||
+ | |||
+ | LIMITATIONS | ||
+ | ============ | ||
+ | * Just works with capital letters and digits now. | ||
+ | * Cant really use the restricted attribute of bind in xforms for example, a question like are you pregnant is valid only for females and should be improve | ||
+ | * Not accurate parsing. | ||
+ | * Selects the field related to the first darkened bubble for a select1 element. | ||
+ | |||
+ | -------------- | ||
- | __Usage and their functionality: | + | __**Detailed description of source**__ |
-------------- | -------------- | ||
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Its a file which takes in the imput of the xml file which has the logical data placement and outputs the xml dump of the parsed data. This particular module tesselates the required images and writes the data to an xmlfile. | Its a file which takes in the imput of the xml file which has the logical data placement and outputs the xml dump of the parsed data. This particular module tesselates the required images and writes the data to an xmlfile. | ||
- | Usage: python parseform.py <xmlinput> <imageinput> <xmloutput> | + | Usage: python parseform.py <imageinput> <> <user> |
<content to be added here> | <content to be added here> | ||
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Training of tesseract automated. | Training of tesseract automated. | ||
+ | |||
+ | Tesseract needs a ' | ||
-> | -> | ||
- | Usage: python train.py < | + | Usage: python train.py < |
+ | |||
+ | instead of | ||
+ | tesseract fontfile.tif fontfile batch.nochop makebox | ||
+ | |||
+ | Tesseract needs to know the set of possible characters it can output. To generate the unicharset data file, use the unicharset_extractor program on the box files generated above: | ||
+ | unicharset_extractor <list of box files> | ||
+ | |||
+ | **Important: | ||
+ | |||
+ | Another error that can occur that is also fatal and needs attention is an error about "Box file format error on line n". If preceded by "Bad utf-8 char..." | ||
+ | |||
+ | -> | ||
+ | When the character features of all the training pages have been extracted, we need to cluster them to create the prototypes. The character shape features can be clustered using the mftraining and cntraining programs: | ||
+ | mftraining -U unicharset -O lang.unicharset fontfile_1.tr fontfile_2.tr ... | ||
+ | or just in most systems | ||
+ | mftraining fontfile.tr | ||
+ | and | ||
+ | cntraining fontfile.tr | ||
+ | |||
+ | Tesseract uses up to 5 dictionary files for each language. Four of the files are coded as a Directed Acyclic Word Graph (DAWG), and the other is a plain UTF-8 text file. To make the DAWG dictionary files, you first need a wordlist for your language. The wordlist is formatted as a UTF-8 text file with one word per line. Split the wordlist into two sets: the frequent words, and the rest of the words, and then use wordlist2dawg to make the DAWG files: | ||
+ | |||
+ | wordlist2dawg frequent_words_list freq-dawg | ||
+ | wordlist2dawg words_list word-dawg | ||
+ | |||
+ | The final data file that Tesseract uses is called unicharambigs. It represents the intrinsic ambiguity between characters or sets of characters, and is currently entirely generated minimally. | ||
+ | |||
+ | And rename all those files generated as < | ||
+ | |||
+ | The resulting lang.traineddata goes in the tessdata(usually / | ||
+ | tesseract image.tif output -l lang | ||
-------------- | -------------- | ||
+ | |||
+ | __upload.py__ | ||
+ | |||
+ | The upload function has been implemented here | ||
+ | |||
+ | |||
+ | -------------- | ||
+ | |||
__xforms2pdf.py__ | __xforms2pdf.py__ | ||