Bibliographic Data Entry · Persian Editorial Automation · Quranic/Arabic AutoCorrect · Web Catalog Extraction · Batch Production Reporting
Microsoft Word VBA · Excel · XML/Reference Data · HTML Processing · Windows Batch · Workflow Optimization
Portfolio positioning
This body of work began as operational automation for high-volume bibliographic and document-processing tasks.
I was supervising a team of approximately 15 data-entry operators who were typing bibliographic and table-of-contents information. Whenever a new collection or typing workflow created a bottleneck, I analyzed the repetitive steps and built small automation tools around the existing work environment—primarily Microsoft Word, VBA, Excel, and later Windows batch processing.
Over time, this evolved into several distinct automation projects:
- Bibliographic Data Entry Productivity Suite
- Persian Editorial & Proofreading Automation
- Quranic & Arabic AutoCorrect / Shorthand Engine
- Web Catalog Extraction & HTML Content Processing Pipeline
- Batch File Inventory & Production Reporting Toolkit
The exact historical number of macros was never formally counted. Many task-specific variants were created for different collections. For portfolio purposes, the safest and most defensible description is:
Approximately 200 distinct/reusable VBA macros, with many additional collection-specific variants and iterations created whenever new workflow bottlenecks appeared.
Some historical variants may have existed in much larger numbers, but they should not be presented as a verified count.
1. Portfolio Summary
This family of projects is best described as:
A collection of document-processing and productivity automation systems designed to accelerate bibliographic data entry, multilingual text preparation, reference expansion, content extraction, and production reporting.
The central engineering principle was consistent across all of them:
Observe the Real Workflow
↓
Identify Repetitive / Slow Steps
↓
Automate the Bottleneck
↓
Keep the Existing User Tool
↓
Reduce Keystrokes / Navigation / Rework
↓
Repeat for the Next Collection
The work was not a single fixed macro package built once.
It was a continuous process-optimization practice.
2. Historical Operating Context
A team of approximately 15 employees was responsible for entering bibliographic or table-of-contents information.
The work environment was heavily centered on Microsoft Word.
Typical problems included:
- repetitive chapter and section numbering,
- Persian/Arabic/English mixed text,
- difficult Arabic diacritics and special characters,
- repetitive page-number entry,
- repetitive religious references,
- repeated editorial corrections,
- extracting already-existing catalog text from websites,
- counting processed files/pages for reporting,
- maintaining consistent output across many operators.
Instead of replacing the entire user workflow with a new application, I extended the existing tools the team already used.
That was an important design decision:
automation inside the operator’s current working environment was often more effective than forcing a completely new system onto the team.
3. Project A — Bibliographic Data Entry Productivity Suite
Professional Title
Microsoft Word VBA Productivity Suite for Bibliographic Data Entry
Short Version
~200 Reusable Macros · Multilingual Cataloging · Workflow Automation · High-Volume Typing
4. Purpose
The purpose of this project was to increase the typing speed and consistency of a 15-person bibliographic data-entry team.
The workflow was observed continuously.
Whenever a new collection introduced a slow repetitive action, a macro or helper workflow was created for it.
This means the project behaved more like an evolving internal productivity platform than a one-time script.
5. Example — Sequential Heading Generator
An operator might initially type:
مقدمه مولف
پیشگفتار: درآمدی بر تاریخ کتاب و کتابخانه
Instead of typing every chapter heading manually, a macro asked a sequence of questions such as:
Base word?
→ فصل
How many?
→ 2
Numbers or words?
→ Words
The output became:
فصل اول
فصل دوم
This can be described as a:
Parameterized Sequential Heading Generator
6. Hierarchical Structure Generator
A more advanced version could also generate nested structures such as:
فصل اول
1.1
1.2
1.3
فصل دوم
2.1
2.2
2.3
or other numbering patterns defined for the collection.
This was not only a typing shortcut.
It automated part of the document structure itself.
Professional description:
Automatic hierarchical numbering and section generation for bibliographic tables of contents.
7. Arabic Structure Generation
Separate macros were created for Arabic output.
Examples of the type of content involved:
الباب الأول
الفصل الأول
الفصل الثاني
The purpose of keeping Arabic logic separate was to support different language conventions and output patterns.
8. English Structure Generation
Equivalent generation logic also existed for English structures.
Examples:
Chapter One
Chapter Two
or:
1.1
1.2
2.1
2.2
This made the suite multilingual rather than Persian-only.
9. Configurable Character Shortcut Mapping
A major speed problem was entering difficult Arabic characters and diacritics using repeated Shift/key combinations.
A configurable shortcut model was created.
Conceptually:
1 → أ
2 → إ
3 → ؤ
4 → ئ
The actual mapping could be chosen according to the operator’s needs.
This reduced interruption of typing rhythm.
Professional description:
User-configurable text-expansion shortcuts for difficult Arabic and Persian characters.
10. Why This Mattered
For a single character, the time saving seems small.
For thousands of entries, the effect compounds.
The real optimization target was:
Keystrokes per Entry
×
Entries per Day
×
15 Operators
Reducing even a few actions per entry can substantially increase total throughput.
11. Cursor Navigation Automation
After the main text of a table of contents had been entered, operators needed to add page numbers.
One macro moved the cursor directly to the appropriate end position for the next entry.
This removed repetitive:
- mouse movement,
- arrow-key navigation,
- End-key navigation,
- manual positioning.
Conceptually:
Current Entry
↓
Macro
↓
Next Page-Number Position
12. Context-Aware Page Number Entry
Another macro reduced repeated number entry.
The previous page number or stable numeric context could be preserved so the operator only entered the changing part.
This reduced redundant typing when numbers followed predictable patterns.
A defensible professional label is:
Context-aware numeric entry assistance for sequential page-number workflows.
13. Domain-Specific Reference Expansion
The suite also included shorthand expansion for religious and bibliographic references.
For Quran references, the operator could enter a compressed form such as:
SurahName + VerseNumber
without unnecessary spacing.
If commentary/tafsir information was needed, a short code could be appended.
Conceptually:
Compact Input
↓
Parse Surah
↓
Parse Verse
↓
Optional Commentary Code
↓
Expand to Standard Reference
This turned repetitive reference typing into a structured shorthand system.
14. Religious Source Reference Automation
Similar logic was applied to recurring religious-source references.
Examples included material from sources such as:
- Quran,
- Tafsir/commentary references,
- Nahj al-Balagha-related content,
- other repeated domain-specific source patterns.
The exact historical shorthand for every source should only be documented where surviving source code or examples exist.
15. Macro Taxonomy
The Word/VBA suite can be classified into several functional groups:
Bibliographic Productivity Suite
│
├── Structural Generation
│ ├── Persian Headings
│ ├── Arabic Headings
│ ├── English Headings
│ └── Hierarchical Numbering
│
├── Fast Typing
│ ├── Character Shortcuts
│ └── Text Expansion
│
├── Navigation
│ └── Cursor Positioning
│
├── Numeric Assistance
│ └── Page Number Continuation
│
├── Domain-Specific Expansion
│ ├── Quran References
│ ├── Tafsir References
│ └── Religious Source Patterns
│
└── Collection-Specific Utilities
└── New Macros Created When Bottlenecks Appeared
16. Development Style
The project followed a highly practical optimization loop:
New Collection Starts
↓
Observe Team
↓
Find Slow Operation
↓
Build VBA Macro
↓
Use in Production
↓
Adjust / Duplicate / Specialize
This explains why the total number of historical variants was never reliably counted.
17. Conservative Macro Count
For public portfolio use:
Approximately 200 distinct/reusable macros
is a much stronger claim than trying to present an unverified total.
Additional statement:
Many more collection-specific variants and modified copies were created during production, but they were not formally versioned or counted.
18. Project B — Persian Editorial & Proofreading Automation
Professional Title
Dictionary-Assisted Persian Proofreading & Editorial Automation
This work evolved from the broader Word automation environment into a more specialized editorial project.
19. Purpose
The goal was to automate parts of Persian proofreading and editorial correction using dictionary/reference data.
The user described the project as using Dehkhoda as a reference source for identifying spelling issues and assisting editorial cleanup.
The surviving screenshots also show a separate editorial workspace with files such as:
AutoCorrect Backup Document.docx
AutoCorrect.dotm
Check.xlsx
Final.xlsx
Quran.xlsx
repeat1.xlsx
repeat2.xlsx
Swap 1.xlsx
Swap 2.xlsx
These files support the existence of a larger rule/reference-driven document-processing workflow, although the exact purpose of every file cannot be established from the screenshots alone.
20. Conceptual Workflow
Word Document
↓
Text Processing
↓
Dictionary / Reference Data
↓
Detection / Comparison
↓
Correction Assistance
↓
Edited Output
This project should be presented as an editorial automation system, not as a full modern NLP engine unless the surviving source confirms such behavior.
21. Why It Became a Separate Project
The original productivity macros were focused on speed.
The proofreading/editorial project had a different objective:
Typing Productivity
≠
Text Quality Control
Once dictionary-based checking and editorial rules became significant, it deserved its own project boundary.
22. Portfolio Positioning
Strong wording:
Developed a Microsoft Word/VBA-based Persian editorial automation workflow using external reference data to assist spelling detection, normalization, and repeated text-cleanup operations.
Avoid claiming:
- AI proofreading,
- semantic grammar correction,
- complete linguistic analysis,
unless source evidence exists.
23. Project C — Quranic & Arabic AutoCorrect / Shorthand Engine
Professional Title
Arabic & Quranic Text Input Acceleration and AutoCorrect System
This project also evolved from the earlier bibliographic macro environment.
24. Surviving Evidence
The screenshots show historical files including:
Quran.xlsx
QuranArabicText.doc
AutoCorrect.dotm
Quran.xlsx
as well as Word output containing Arabic forms and diacritics.
Another screenshot shows a VBA project with a visible macro module.
This is strong evidence that the Arabic/Quranic workflow was developed as a real reusable automation environment.
25. Core Idea
The system was designed to reduce the cost of entering:
- Arabic characters,
- Arabic diacritics,
- repeated religious phrases,
- Quranic references,
- commentary references,
- frequently recurring text forms.
Conceptually:
Short Input
↓
Rule Lookup
↓
Expansion
↓
Correct Arabic/Quranic Output
26. Diacritic Acceleration
Typing Arabic diacritics through traditional keyboard combinations is slow.
Instead, simplified key mappings were used.
The screenshots showing repeated Arabic forms with different diacritics are useful historical evidence for this workflow.
27. AutoCorrect as an Input Engine
Microsoft Word AutoCorrect could be repurposed as a lightweight text-expansion engine.
Conceptually:
Shortcut
→ Expansion
but the project extended this idea into a domain-specific system tailored for religious and Arabic text production.
28. Reference Data
Excel files such as Quran.xlsx were used as structured reference data in the broader workflow.
The exact columns and mapping behavior should be described from the file/source if reconstructed later.
For portfolio purposes, it is safe to describe them as:
supporting reference datasets for Quranic/Arabic text automation
rather than claiming a specific schema that is not visible.
29. Productization
The user explicitly separated this religious-text automation from the original operator macros and treated it as an independent AutoCorrect project.
That makes it a standalone portfolio item.
30. Project D — Web Catalog Extraction & HTML Content Processing Pipeline
Professional Title
Web Catalog Extraction & HTML Content Processing Pipeline
This project began when some users no longer needed manual transcription because the table-of-contents text already existed on websites.
The problem changed from:
Type the Content
to:
Extract Only the Useful Content
31. HTML Grabber
A grabber/extractor was written to retrieve the relevant web pages.
This created a local collection of HTML documents.
32. VBA-Based HTML Processing
A second stage used VBA to process the downloaded pages.
The surviving screenshot shows VBA code that:
- iterates through numbered
.htmfiles, - opens them programmatically,
- sets document direction,
- searches for text,
- and applies repeated cleanup logic.
The visible code includes operations such as:
Document.Open
Selection.Find
Text = ...
Replacement.Text = ""
This supports the description of a batch HTML-cleaning workflow.
33. Targeted Content Extraction
The user described the goal as preserving the div containing the catalog/table-of-contents information and removing the rest.
Conceptually:
Downloaded HTML
↓
Open Page
↓
Identify Catalog DIV / Relevant Content
↓
Remove Irrelevant Page Content
↓
Preserve Required Text
34. Why This Is a Separate Project
This was no longer a typing macro.
It was a small ETL/content-extraction pipeline:
Extract
Transform
Prepare
The output could then be reused in the bibliographic workflow.
35. Portfolio Description
Built a lightweight catalog extraction pipeline that retrieved web pages and batch-processed the resulting HTML files through VBA, preserving the relevant catalog/table-of-contents content while removing unrelated page markup and text.
36. Project E — Batch File Inventory & Production Reporting Toolkit
Professional Title
Windows Batch File Inventory & Production Reporting Toolkit
This project was created for operational reporting.
37. Original Reporting Need
The user described earlier variants that inspected processed files, determined production quantities such as page/file counts, and generated a text result that could be imported into Excel.
The oldest versions are no longer available.
Therefore the portfolio should distinguish:
Original Historical Behavior
from:
Surviving Later Example
38. Surviving Batch Script
The surviving screenshot shows a Windows Batch file named:
count.bat
The visible code recursively iterates folders and files and writes counts to a text file.
The code contains logic similar to:
for /f ...
dir /a:-d /b
count=count+1
echo folder : count >> textfile.txt
This directly supports the claim that the toolkit produced machine-readable operational summaries.
39. Reporting Workflow
Conceptually:
Folder / File Structure
↓
Batch Script
↓
Recursive Inspection
↓
Count / Production Metric
↓
Text Report
↓
Excel
↓
Operational Reporting
40. Excel as the Analysis Layer
The text report was designed so it could be opened or imported into Excel.
This created a simple but effective pipeline:
Windows Filesystem
→ Batch
→ TXT
→ Excel
without requiring a dedicated reporting application.
41. Later Reuse in Digitization / Photography
The user described reusing the same reporting concept later in document photography/digitization workflows.
This should be presented carefully:
The same batch-reporting pattern was later reused in digitization/photography production workflows.
That is stronger and more accurate than claiming the surviving count.bat is itself the original photography tool.
42. Cross-Project Architecture
These projects can be understood as one automation family:
Document & Publishing Automation
│
├── Human Input Optimization
│ └── Word VBA Productivity Suite
│
├── Text Quality Automation
│ └── Persian Editorial / Proofreading
│
├── Domain-Specific Text Expansion
│ └── Quranic / Arabic AutoCorrect
│
├── Web Content Acquisition
│ └── HTML Catalog Extraction
│
└── Production Reporting
└── Batch + TXT + Excel
43. Common Engineering Philosophy
Across all projects, the same design philosophy appears repeatedly:
Do not automate the entire world. Automate the exact repetitive step that is slowing the real user down.
Examples:
- chapter generation,
- diacritic input,
- page-number entry,
- reference expansion,
- website catalog extraction,
- file counting,
- report generation.
44. Why This Portfolio Family Is Strong
These projects demonstrate experience in:
- process analysis,
- VBA,
- Microsoft Word automation,
- Excel-assisted processing,
- text normalization,
- multilingual input,
- Arabic/Persian handling,
- HTML batch processing,
- file-system automation,
- Windows Batch,
- data extraction,
- reporting,
- operator productivity,
- iterative workflow optimization.
45. It Was Not Just “Macros”
The weak description would be:
“I wrote Word macros.”
The stronger and more accurate description is:
I designed a family of document-processing automation tools around Word, VBA, Excel, HTML, and Windows Batch to accelerate high-volume bibliographic entry, multilingual text preparation, editorial processing, content extraction, and production reporting.
46. Evidence from Surviving Artifacts
The current surviving screenshots provide evidence for several parts of the story.
A. Quran / Arabic Workflow
Visible files include:
Quran.xlsx
QuranArabicText.doc
AutoCorrect.dotm
and Word screenshots showing Arabic text with multiple diacritic forms.
B. Editorial Workspace
Visible files include:
AutoCorrect Backup Document.docx
Check.xlsx
Final.xlsx
repeat1.xlsx
repeat2.xlsx
Swap 1.xlsx
Swap 2.xlsx
This supports a rule/reference-driven processing workflow.
C. VBA Automation
A screenshot shows a Word VBA project and macro source code processing multiple HTML files.
D. Batch Reporting
A surviving count.bat screenshot shows recursive file counting and text output.
47. Historical Evidence Boundaries
The screenshots do not prove every historical macro or every original version.
Therefore the website should distinguish:
- surviving evidence,
- user-described historical behavior,
- modern interpretation.
Do not present an exact macro count beyond the conservative public estimate.
48. Recommended Public Numbering
Use:
~200 distinct/reusable macros
Optional clarification:
with many additional task-specific variants created during production
Avoid:
1000 macros
because that number was never formally tracked.
49. Recommended Website Master Description
Document & Publishing Automation
I developed a family of document-processing automation tools around Microsoft Word, VBA, Excel, HTML processing, and Windows Batch while supervising a 15-person bibliographic data-entry workflow.
Rather than replacing the operators’ existing tools, I observed where repetitive work was slowing them down and automated those exact steps.
Over time, the work grew into several independent systems:
- a Word/VBA productivity suite with approximately 200 distinct reusable macros,
- Persian editorial and proofreading automation,
- Quranic/Arabic AutoCorrect and shorthand expansion,
- a web catalog extraction and HTML-cleaning pipeline,
- and a batch-based production reporting toolkit.
The Word automation included multilingual heading generation, hierarchical numbering, difficult-character shortcuts, cursor navigation, page-number assistance, and shorthand expansion for repeated religious references.
Additional projects evolved from the same environment: dictionary-assisted editorial checking, Quranic/Arabic text automation, extraction of catalog content from downloaded HTML pages, and filesystem-based reporting exported to text and Excel.
The common goal across all of these systems was simple:
reduce repetitive operator work without forcing users to abandon the tools they already knew.
50. Website Project Cards
Card 1
Bibliographic Data Entry Productivity Suite
Microsoft Word VBA · ~200 Reusable Macros · Multilingual Workflow Automation
Developed an evolving Word/VBA productivity layer for a 15-person bibliographic data-entry team, including automatic heading generation, hierarchical numbering, multilingual typing assistance, cursor navigation, page-number entry, and domain-specific text expansion.
Card 2
Persian Editorial & Proofreading Automation
Word VBA · Reference Data · Text Normalization
Developed a Persian editorial automation workflow using dictionary/reference data to assist repetitive spelling, normalization, and document-cleanup operations.
Card 3
Quranic & Arabic AutoCorrect Engine
Arabic/Persian Input · AutoCorrect · Diacritics · Shorthand Expansion
Built a domain-specific text-expansion and AutoCorrect workflow for Arabic and Quranic content, reducing the effort required to enter difficult characters, diacritics, repeated phrases, and structured references.
Card 4
Web Catalog Extraction Pipeline
HTML Grabber · VBA · Batch Content Processing
Built a lightweight extraction workflow that collected web pages and batch-processed them to retain catalog/table-of-contents content while removing unrelated page material.
Card 5
Batch File Inventory & Production Reporting
Windows Batch · TXT · Excel
Created batch-based reporting utilities that inspected production folders, calculated file/production metrics, and exported structured text results for Excel-based reporting; the same pattern was later reused in digitization workflows.
51. CV Master Entry
Document & Publishing Automation — Word VBA / Excel / HTML / Windows Batch
Developed a family of document-processing automation tools while supervising a 15-person bibliographic data-entry workflow. Built approximately 200 distinct reusable VBA macros plus collection-specific variants for multilingual heading generation, hierarchical numbering, typing shortcuts, cursor navigation, page-number assistance, and domain-specific text expansion. Extended the work into Persian editorial automation, Quranic/Arabic AutoCorrect, HTML catalog extraction, and batch-based production reporting.
52. Short CV Entry
Built ~200 reusable Word/VBA automation macros for a 15-person bibliographic data-entry team and developed related Persian proofreading, Arabic/Quranic AutoCorrect, HTML extraction, and batch-reporting tools.
53. Interview Story
Situation
A 15-person team was entering large amounts of bibliographic and table-of-contents data in Microsoft Word.
Different collections introduced different repetitive typing patterns.
Task
Increase operator throughput without forcing the team to move to a completely new software environment.
Action
I observed the operators’ actual workflow and created targeted VBA automations whenever a bottleneck appeared.
Examples included:
- automatic Persian/Arabic/English chapter generation,
- hierarchical numbering,
- user-defined shortcuts for difficult Arabic characters,
- cursor positioning for page-number entry,
- context-aware numeric entry,
- Quran/commentary shorthand expansion.
As additional needs appeared, the work expanded into separate projects for Persian editorial processing, Quranic/Arabic AutoCorrect, website catalog extraction, and batch-based production reporting.
Result
The team’s repetitive work was increasingly shifted from manual keystrokes and navigation into reusable automation.
The total number of historical variants was not formally tracked, so the portfolio uses the conservative figure of approximately 200 distinct/reusable macros.
54. Technical Interview Answer
“The important part was not the number of macros. I treated the data-entry operation as a workflow-optimization problem. Whenever a new collection started, I watched where operators lost time and built a small automation for that bottleneck. Some macros generated hierarchical headings, some replaced difficult Arabic keystrokes with shortcuts, some navigated directly to the next page-number position, and some expanded shorthand into complete Quranic or bibliographic references. The same approach later evolved into separate editorial, extraction, and reporting tools.”
55. Why the Approach Was Effective
The users already knew Word.
Replacing Word entirely would have introduced:
- training cost,
- workflow disruption,
- adoption resistance.
By embedding automation into Word, the system gained:
- immediate usability,
- low training overhead,
- fast iteration,
- direct operator feedback.
56. Human-Centered Automation
This is a useful example of human-centered automation.
The workflow was not designed in isolation.
It was built around observed operator behavior.
Conceptually:
Human Task
↓
Observe Friction
↓
Automate Repetition
↓
Human Keeps Control
57. Modern Interpretation
Today, some of these tools might be implemented using:
- dedicated web applications,
- Python text-processing pipelines,
- browser automation,
- OCR/NLP services,
- database-backed rule engines,
- Power Automate,
- Office Scripts.
But the historical implementation is valuable because it solved the problem using the tools actually available in the operational environment.
58. Modern V2 — Bibliographic Automation
A modern rebuild could use:
Web UI
+
Rule Engine
+
Reference Database
+
Keyboard Shortcut Layer
+
Validation Service
while preserving the original productivity goals.
59. Modern V2 — Editorial Engine
Potential architecture:
Document
↓
Tokenizer / Normalizer
↓
Dictionary / Rule Engine
↓
Candidate Corrections
↓
Human Review
This would preserve the human-in-the-loop editorial model.
60. Modern V2 — Quranic Reference Engine
Potential architecture:
Shortcut Parser
↓
Reference Resolver
↓
Quran / Commentary Dataset
↓
Standardized Citation Output
61. Modern V2 — Web Extraction
Potential architecture:
HTTP Fetch
↓
HTML Parser
↓
CSS / DOM Selector
↓
Content Normalizer
↓
Structured Output
This would replace the old Word/VBA HTML-cleaning step while preserving the same logic.
62. Modern V2 — Reporting
Potential architecture:
Filesystem Scanner
↓
Metadata Collector
↓
CSV / JSON
↓
Dashboard / Excel
63. What Not to Overclaim
Do not claim:
- an exact total number of all historical macros,
- AI-based proofreading,
- full linguistic NLP,
- a complete web crawler framework,
- exact functionality of every surviving Excel file,
- that the surviving
count.batis the exact original page-counting version.
Use evidence-based wording.
64. Strong Final Portfolio Statement
I designed an evolving document-automation ecosystem around Microsoft Word, VBA, Excel, HTML processing, and Windows Batch while supervising a 15-person bibliographic data-entry operation. Approximately 200 distinct reusable macros were created to eliminate repetitive work, with many additional collection-specific variants. The work later expanded into separate Persian editorial, Quranic/Arabic AutoCorrect, web-content extraction, and production-reporting projects.
65. One-Line Summary
Built a document-automation ecosystem that converted repetitive bibliographic, multilingual, editorial, extraction, and reporting tasks into reusable operator tools.
66. Suggested Portfolio Group Name
Document & Publishing Automation
Suggested child projects:
1. Bibliographic Data Entry Productivity Suite
2. Persian Editorial & Proofreading Automation
3. Quranic & Arabic AutoCorrect Engine
4. Web Catalog Extraction Pipeline
5. Batch File Inventory & Production Reporting
This structure is stronger than presenting everything as one generic “Word Macro” project because each child project represents a different engineering problem and a different type of automation.